THISR modular kits provide the tools needed to capture the first-person view on the battlefield. (Photo: Bruce Donaldson, THISR team leader, Red Hen Systems)
The Tactical Handheld Intelligence Surveillance Reconnaissance (THISR) by Red Hen Systems is an advanced intelligence, surveillance and reconnaissance (ISR) asset providing a real-time solution to operators and mission teams.
The modular kits provide the tools needed to capture the first-person view on the battlefield. The THISR is a custom integration of cameras, a laser rangefinder, GPS unit and software linked through Red Hen System’s VMS-333 mapping system.
THISR is the integration of three core collection technologies:
Random Access Full Motion Video (RAFMV) with mapping integration
360° immersive rendering
light UAV/UAS
Together, all three technologies provide critical information to the operator for use in planning superior missions, enhancing situational awareness and protecting forces, the company said.
The kits offer near-real-time dissemination and surveillance, and can be integrated with other technologies.
The THISR options.
Mapping system. The VMS-333 encodes multiple geo-referenced sensor metadata records into a single data stream and combines this metadata with photographic and video imagery. Data multiplexing capabilities are available for two different mission types–nadir and oblique ground observation missions.
The nadir mission provides an automated process to create a seamless orthogonal geo-referenced photographic mosaic of the entire flight path that can be used to produce 3D terrain models of the ground below.
The oblique mission provides the functionality to take at-will photographs of ground-based areas of interest from a handheld SLR camera, and geo-reference these photographs with the location of the ground target using coupled laser range finder technology.
How four frequencies help when the ionosphere is disturbed
The authors explore how cycle slips in Galileo carrier-phase measurements can be more effectively detected using four frequencies.
INNOVATION INSIGHTS with Richard Langley
MORE SATELLITES OR MORE SIGNALS? That was the question put to the delegates at GNSS Election ’08, the stimulating and amusing entertainment provided at the GPS World Leadership Dinner held in conjunction with The Institute of Navigation’s meeting in Savannah in September 2008.
During the debate ahead of the election, the Satellite Party advocated that the GNSS user community would be better served by more satellites than more signals. They argued that more satellites (more than those in the operational GPS constellation) would enable more continuous and reliable positioning in cities, mountainous areas and other difficult environments and that the legacy GPS signals were sufficient. Greg Turetsky, one of their candidates, stated, “I would maintain from an economic standpoint that it’s far more cost-effective for our constituents to have more of the same satellites to give them more of the same services that they enjoy today, in more areas, rather than creating new things for which they have no use.”
The Signal Party, on the other hand, advocated for more signals with receivers capable of using them to provide high accuracies for a wide spectrum of GNSS uses. Signal Party candidate Javad Ashjaee opined, “We are the party of building roads, generating accurate maps, growing your food by automating agriculture, synchronizing your power stations. We are even working on automatically landing aircraft to use the air space more efficiently.”
Although contested, the election was won by the Satellite Party, 62 votes to 46. But clearly, both sides offered beneficial advances to the GNSS user community, so why not work together, have the parties enter into an alliance, and provide both more satellites and more signals?
Fast forward to 2016. The alliance has come to pass and we have the best of both worlds. We have two complete GNSS constellations, GPS and GLONASS, with two more, Galileo and BeiDou, on track for completion within the next few years. We also have regional systems either supplying an independent local positioning service or augmenting GPS with NavIC (also known as the Indian Regional Navigation Satellite System) and QZSS, respectively. Not to mention a growing number of satellite-based augmentation system satellites. When I compiled The Almanac for the August issue, there were over 100 GNSS satellites transmitting signals to users. And not only more signals from more satellites, but more technologically advanced signals on more frequencies.
The plethora of signals now being transmitted by GNSS satellites is already leading to further advances in positioning, navigation and timing—even before full constellations transmitting those signals are in place. A good case in point is Galileo’s Open Service, which is transmitted in the E1 and E5 bands. A modified version of binary-offset-carrier (BOC) modulation, called Alternative BOC or AltBOC, is used to generate the wideband E5 signal. Its structure is such that a receiver can track and make measurements on just the lower frequency part of the signal centered on 1176.450 MHz (E5a), just the upper frequency part centered on 1207.140 MHz (E5b), the whole AltBOC signal centered on 1191.795 MHz (E5a+b), or any combination of these including all three. Using all three together with the E1 signal provides us with a four-frequency positioning capability. What’s the benefit of using four frequencies? There are several, but in this month’s column, a recently graduated award-winning Belgian student and her supervisor tell us how cycle slips in Galileo carrier-phase measurements can be more effectively and efficiently detected using four frequencies.
The availability of data offered in the Galileo GNSS Open Service on four carrier frequencies opens the way to new multi-frequency solutions for civil users. In the research reported in this article, we focused on one of the consequences of signal tracking loss, the appearance of cycle slips, and how the use of the four frequencies can help in their detection.
Cycle-slip detection is a key issue for high-precision positioning applications. Any users in need of determining a precise and reliable position must be aware of the potential presence of cycle slips in their data, since they compromise data quality.
Traditionally, two carrier frequencies were used for positioning; for instance, the GPS L1 and L2 frequencies. More recently, three-carrier positioning has allowed enhanced precision and accuracy. Though using a third carrier frequency has allowed us to partially solve the cycle-slip detection issue, existing procedures are still lacking in some aspects. One of today’s main challenges is cycle-slip detection under high ionospheric activity, which is why we focused on this specific case study. And since the use of three frequencies helps to improve reliable cycle-slip detection, might not the use of an additional fourth frequency further improve detection capability? Since Galileo supplies four frequencies in its Open Service, we thought we might be able to improve cycle-slip detection algorithm performance once more.
Framework. In this article, a new quad-frequency cycle-slip detection algorithm is introduced — seemingly, an unexplored track in the literature until now. The algorithm uses undifferenced carrier-phase observations from a single-station static receiver. First developed for post-processing, the algorithm also has been adapted to real-time applications. This algorithm aims to improve cycle-slip detection under high ionospheric activity.
CYCLE SLIPS
Though code (pseudorange) measurements are commonly used for standard positioning, any precise positioning application needs to use carrier-phase measurements, due to their better quality. Unfortunately, the latter are potentially subject to cycle slips, generating a constant bias in data and, if undetected and uncorrected, impacting the inferred positioning.
Carrier-phase measurements are made by observing the beat phase, that is, the difference between the received carrier from the satellite and a receiver-generated replica. At the first observation epoch, only the fractional part of this beat phase can be measured, but the integer offset between the satellite signal and the receiver’s replica is unknown. This integer number of cycles is called the initial phase ambiguity and remains constant during the observation period.
The carrier-phase observable (between a satellite i and a receiver p), in meters, is given by the following equation:
(1)
where the subscript fk indicates the term dependency on the frequency and Φ on the carrier-phase observable. G is the geometric term (that is, a function of the geometric range between the receiver and the tracked satellite, the tropospheric delay, and satellite and receiver clock bias), I is the ionospheric delay, M is the multipath error, HW stands for satellite and receiver hardware delays, c is the vacuum speed of light, N is the initial phase ambiguity, and ε is the random error (also called phase noise).
At the first observation epoch, an integer counter is initialized, and as the tracking goes on, it is incremented by one cycle whenever the beat phase changes from 2π to 0. If the receiver — even briefly — loses track on the signal, the counting is suspended and an integer number of cycles is lost. This loss can result from various causes (signal obstruction, rapid change in the carrier-phase observable, and so on).
In the observation equation, the cycle slip will appear as a change in the value of the initial phase ambiguity. Thus, a one-cycle slip will involve a phase measurement shift of about 20 centimeters (equal to the carrier wavelength), depending on the affected carrier frequency. The cycle-slip size can be any value from one to thousands of cycles.
Ionospheric delay is the only term that could possibly be confused with a small cycle slip. Indeed, during an ionospheric perturbation event, this delay variation between two observation epochs (spaced at 30-second intervals, say) often reaches 20 centimeters (the size of a one-cycle slip in the phase measurement) or more. The ionosphere activity has two main consequences. Firstly, as mentioned before, slips can be hidden in observation noise (including ionospheric variability) and not detected. Secondly, received signal variability can cause loss of lock and thus cycle slips.
A lot of different configurations can arise when the signal is lost. Signal tracking can be interrupted on one single carrier resulting in an isolated cycle slip (ICS) or simultaneously on multiple carriers. In the second case, the slip magnitude on the different carriers can be the same (simultaneous cycle slips of the same magnitude, or SCS-SM) or different (simultaneous cycle slips of different magnitudes, or SCS-DM).
Detection History. The first cycle slip detection algorithm using undifferenced observations, Turbo Edit, was developed in 1990 by Geoff Blewitt. Code and phase measurements from two carrier frequencies are used. It has been implemented in many data preprocessing programs, such as GIPSY-OASIS II, PANDA and Bernese. The Turbo Edit algorithm has been enhanced numerous times. In its latest version, it was adapted to detect cycle slips under high ionospheric activity, but it is still a dual-frequency technique.
Availability of a third, simultaneous signal frequency permits the development of new combinations of observables. A low-noise phase-only combination eliminating geometric as well as first-order ionospheric terms was developed by Andrew Simsky and applied to cycle-slip detection. Studies have also been made to determine the best combinations to be used in triple-frequency positioning, and subsequently in cycle-slip detection and correction algorithms. These algorithms use both code and phase measurements, as well as a triple-frequency method developed by Maria Clara de Lacy and colleagues.
Concern about cycle slips and the relationship with the ionospheric signature in data is trending. In 2011, Zhizhao Liu published a paper on using the rate of change of total electronic content to detect cycle slips. On the other hand, after studying ionospheric cycle slips, Simon Banville and Richard Langley concluded in a paper published in 2013 that the “increased measurement noise associated with an active ionosphere makes correcting cycle slips an ongoing challenge, which requires further investigation,” while Xiaohong Zhang and colleagues, in a paper published in 2014, came to the same conclusion while trying to repair cycle slips during scintillation events. See Further Reading for a list of the highlighted papers in the history of cycle-slip detection and correction.
QUAD-FREQUENCY ALGORITHM
Cycle-slip detection techniques use testing quantities (where the cycle slip is represented by a jump or significant change in the quantity). These are associated with a discontinuity detection algorithm, which aims to locate the jump.
Testing Quantities. Testing quantities are linear combinations of observations. They differ in several aspects: the observables used (in our case, only phase measurements), the number of carrier frequencies used and inner properties of the combination (geometry-free, ionosphere-free and the noise level on the combination).
In our study, we assumed values for the noise on Galileo carrier-phase measurements as given in TABLE 1.
Table 1. Frequencies available in the Galileo Open Service.
Triple-Frequency Simsky Combination. Our algorithm is mainly based on exploiting the triple-frequency Simsky combination. It is a geometry-free and ionosphere-free carrier-phase combination, in meters, as shown in Equation 2.
(2)
When four frequencies are available, four triple-frequency combinations can be computed. Two of them are sufficient to detect slips on any of the four frequencies.
The combination choice must first depend on its precision (given by σS in TABLE 2), obtained by applying the variance-covariance propagation law to raw measurement noise (see Table 1). Precision is not the only factor to be taken into account in the choice of suitable combinations. In each combination, carrier frequencies have different impacts due to their different wavelengths: the impact of a one-cycle-amplitude slip on the E1 frequency will indeed not be the same as the one on E5a, E5b or E5a+b (see Table 2). The smallest impact on a given combination is always the most difficult one to detect.
Table 2. Simsky combinations.
Therefore, the efficiency of a given combination will depend on both the effect of the smallest cycle slip and the combination precision (given by the standard deviation): the higher the ratio between them, the more efficient the combination.
Among the four combination possibilities, the two highest ratios are those formed by the E5a-E5b-E5a+b and E1-E5a-E5b combinations. These will thus be the ones used in our algorithm.
The Simsky combination allows us to detect ICS as well as SCS-DM cycle slips. Nevertheless, this combination is insensitive to SCS-SM slips on all four frequencies (which is a rare phenomenon). We will therefore have to add another testing quantity to our algorithm.
Dual-Frequency, Geometry-Free Combination. The dual-frequency, geometry-free (GF) combination, in meters, allows us to detect SCS-SM slips. It can be computed as follows:
(3)
Unfortunately, the raw dual-frequency, geometry-free combination is affected by ionospheric delay. To mitigate the ionospheric smooth trend, a fourth-order time difference is computed. Still, the result suffers from rapid variations of ionospheric delay.
When four frequencies are available, six dual-frequency combinations can be computed. One is sufficient to detect the presence of simultaneous cycle slips of the same magnitude. The choice will again depend on the ratio between combination precision and the smallest effect of simultaneous one-cycle slips.
On the one hand, differencing the combination results affects precision. On the other hand, the cycle slip, thus the smallest effect to detect, will be amplified by high-order differencing. The best ratio is obtained with a fourth-order difference (see TABLE 3), even if a smooth variation due to the ionosphere is already removed in the second-degree differencing (see Figure 1).
Even if one combination is sufficient, our approach will use two of them to double check their outputs: E1-E5a and E1-E5a+b, since they offer the best ratios.
Detection Method. To detect a discontinuity due to a cycle slip in the testing quantity, it is necessary to establish detection thresholds. Thresholds are one of the key parameters in cycle-slip detection, since they lead to the decision on the presence of a cycle slip or not. If the threshold is too restrictive, some real slips can be missed (a false negative). On the other hand, if it is not restrictive enough, discontinuities that do not match with a cycle slip could be abusively detected (a false positive).
It is important to notice, as our study highlights, that there is no perfect threshold that suits all the needs and constraints. The choice must be made considering the positioning application at hand. Threshold values given in this article are representative and were empirically determined to be optimal with respect to our goal of cycle-slip detection under high ionospheric activity. Results and further discussions about different thresholds can be found in the first author’s thesis (see Further Reading).
Cycle slips will affect the raw Simsky combination by a shift in the mean combination value, whereas the time-differenced one will be affected by a spike.
Detection Using Simsky Combination. Cycle-slip detection on the triple-frequency Simsky combination is performed in two cascading steps (see FIGURE 2).
FIGURE 2. Detection method for the Simsky combination.
The first one uses a time-differenced combination to detect potential cycle slips using a 20-observation-sized forward and backward moving average window, in which the mean and standard deviation statistical parameters are computed. The current epoch is compared to the previous ones to detect a spike, which could correspond to a cycle slip. Two types of thresholds are used: statistical (or relative) and absolute.
As shown in FIGURE 3, using a statistical threshold allows us to adapt detection to the inertia of statistical parameters. Assuming the noise on the observations (here, the Simsky combination results) follows a normal distribution, a confidence interval of 3-sigma around the mean includes 95 percent of the observations. Given the ratio of the two Simsky combinations used (computed earlier), the success rate reaches 100 percent for both combinations, which means any ICS and SCS-DM slips on data will be detected for sure (no false negatives). Nevertheless, false positives may occur because 5 percent of the data is statistically outside the 3-sigma bounds.
FIGURE 3. Statistical and absolute thresholds.
To reduce this rate, an absolute threshold is also applied, equal to 0.4 times the smallest impact of a cycle slip on the combination (see Table 2). If we can take Figure 3 as a suitable example of an extreme ionospheric disturbance leading to unusually high variability in combination results, the absolute threshold will most of the time be far higher than the statistical one and will help to reduce the rate of wrong detections.
As an output of this first step, a flag value is assigned to epochs with larger values than both thresholds, and which are therefore potentially affected by cycle slips.
Once the locations of potential slips are achieved, the second step consists in comparing the mean before and after potential cycle slips for the flagged epochs. A second absolute threshold is applied, equal to 0.8 times the smallest effect. If another potential cycle slip is present in the detection window, the size of the detection window will be reduced to avoid calculation of statistical parameters on partially shifted data.
The goal of the first step is to detect potential slips. Therefore, the priority is to avoid missing a real slip with low threshold values, sometimes leading to false positive detection. On the other hand, the second step aims to separate the potential remaining false positives — outlier spikes in the raw combination — from the real cycle-slip shifts on average. The theoretical performance of this two-step approach is 100 percent: neither false positives nor false negatives should be encountered.
Detection Using Geometry-Free Combination.Since the fourth-order differenced geometry-free combination is affected by a residual ionospheric delay, the previous procedure cannot be applied. Like any time-differenced testing quantity, the slip will appear as a spike in the combination. Therefore, there is no way to distinguish cycle slips from outliers by a mean level comparison (second step).
Consequently, the detection method only consists of a forward-and-backward moving average window, in which a 4-sigma confidence interval is compared to the current epoch combination value. Indeed, in this case, we cannot afford to encounter false positives on 5 percent of epochs (induced by the use of a 3-sigma threshold) since no further step can be set up to eliminate remaining false positives.
The theoretical performances of the geometry-free detection method are also expected to reach 100 percent. Again, neither false positives nor false negatives should be encountered. Note that this calculation only takes ratios into account, neglecting the fact that the geometry-free combination is also sensitive to the variability of the ionosphere.
VALIDATION
We have tested the quad-frequency algorithm on 30-second quad-frequency Galileo observations from stations GMSD (in Nakatane, Japan) and NKLG (in Libreville, Gabon). The GMSD observations were used to test algorithm robustness towards simulated particular cases, whereas the NKLG data were used to assess algorithm behavior for cases met in the equatorial area.
Methodology. Cycle slips were artificially inserted into the GMSD data, simulating the following cycle-slip scenarios: ICS, SCS-DM and SCS-SM. The benefit of such a simulation approach is that the algorithm output can easily be compared to the already-known solution. Moreover, these data had been used to determine whether the use of more carrier frequencies could increase cycle-slip detection performance.
We analyzed a 50-day NKLG dataset, covering observations from Jan. 6 to Feb. 1 and from June 24 to July 19, 2014. This sample is made up of various ionospheric states: calm and extreme days, as well as typical equatorial activity. Since the solar cycle peak happened in 2014, data from that year perfectly fits a study of the effects of high ionospheric activity.
We used NKLG raw data to achieve a dual goal. Firstly, we wanted to determine the proportion of epochs for which small cycle slips (one, two or five cycles) couldn’t be distinguished. This was performed by comparing the impact (in meters) of such scenarios to the instantaneous threshold associated with each epoch. In the case of a high cycle-slip detection threshold, potentially present slips of one, two or five cycles couldn’t be detected. The fraction of epochs in a day for which such small cycle slips would not be detected, for each combination used in the algorithm, seemed to be a suitable indicator of algorithm effectiveness in the equatorial area.
Secondly, we analyzed results by visually assessing algorithm output using combination graphics, and tried to answer the following questions: Do flagged epochs seem to be affected by cycle slips? Are there actual cycle slips that remain undetected?
Results. We looked closely at the results of both our simulations and the analysis of raw data.
Simulation of Particular Cases. Compared to equivalent dual- and triple-frequency methods, our new quad-frequency algorithm gave better results: all inserted cycle slips were successfully detected and no false positive were noticed.
NKLG Raw Dataset Analysis. The validation process using NKLG raw data highlights several trends in algorithm results. First of all, it is interesting to notice that the detection of isolated slips as well as slips of different magnitude (using the Simsky combinations) was guaranteed for every observation epoch of every analyzed day. Indeed, Simsky instantaneous thresholds never exceeded the effect of a slip of one-cycle amplitude.
In addition, in 25 percent of the analyzed days, detection of cycle slips of the same magnitude could also be guaranteed. For the remaining days, detection of simultaneous cycle slips whose amplitudes are less than five cycles could not be guaranteed for a few observation epochs, which can reasonably be neglected because of the very small probability of experiencing such exceptional cases. This is due to the impact of ionospheric variability on the geometry-free combination, inducing high instantaneous threshold values.
However, both the Simsky and geometry-free combinations suffer from false positive detection under extreme ionospheric events: if a cycle slip is detected, it sometimes corresponds to an outlier. This side effect is due to the threshold choices we made to match our initial purpose of detecting all cycle slips for sure, rather than risking missing one of them, even if false positives are part of the results list.
FURTHER IMPROVEMENTS
In addition to post-processing applications, we have also considered a real-time adaptation of the algorithm. The real-time constraint impacts both the Simsky and geometry-free detection methods. In this configuration, the statistical window can indeed only move forward, which neglects cycle-slip detection on the first 20 epochs. Further on, the mean level comparison (see the Simsky detection method described earlier) can no longer be considered because the mean following a potential cycle slip cannot be computed in real-time processing. Even if our quad-frequency detection algorithm suffers from the real-time constraint, it still proves efficient if the latter is taken into account for suitable thresholds choices.
Cycle-slip detection is indeed only a first step, and cycle-slip correction should complete the procedure to avoid discontinuities. It should be pointed out, however, that simply being aware of the presence of a cycle slip in a dataset is precious information for a user, and at the corresponding epoch, the parameters in the solution may be reinitialized.
Enhanced with a suitable cycle slip correction method and a real-time feature, our algorithm could be directly integrated into a software receiver, enabling the supply of continuous and corrected data to the user.
CONCLUSION
In this article, we have introduced the first quad-frequency cycle-slip detection algorithm, with an efficiency that is clearly a step forward.
This innovative detection method opens new doors to numerous research and commercial applications. Every Galileo user, whether civil or military, will be able to benefit from better-quality positioning, especially under harsh ionospheric conditions: not only where the ionosphere is particularly restless such as in the equatorial and polar regions, but also at any latitude during an ionospheric disturbance.
With regard to precise positioning, this is yet another step that reinforces Galileo’s competitiveness against other dual- or triple-frequency systems.
ACKNOWLEDGMENTS
This article is based on the paper “Cycle Slips Detection in Quad-Frequency Mode: Galileo’s Contribution to an Efficient Approach Under High Ionospheric Activity,” the winning submission to the 2014–2015 Students’ Contest of the Comité de Liaison des Géomètres Européens in the Galileo, EGNOS, Copernicus category, which was sponsored by the GSA, the European Global Navigation Satellite Systems Agency.
LAURA VAN DE VYVERE received an M.Sc. in geomatics and geometrology from the Université de Liège, Belgium, in 2015. Her master’s thesis was dedicated to Galileo cycle-slip detection under extreme ionospheric activity. In 2015, she joined M3 Systems Belgium in Wavre as a radionavigation project engineer and is currently involved in GNSS reflectometry and GNSS hybridization projects.
RENÉ WARNANT received an M.Sc. in physics in 1988 and a Ph.D. in physics with a specialty in GNSS in 1996, both from the Université catholique de Louvain, Louvain-la-Neuve, Belgium. He started his career as a geodesist at the Royal Observatory of Belgium in 1988. Since June 2011, he is a full-time professor and head of the Geodesy and GNSS Laboratory at the University of Liège where he is responsible for education in the field of space geodesy and GNSS.
“A New Automated Cycle Slip Detection and Repair Method for a Single Dual-Frequency GPS Receiver” by Z. Liu in Journal of Geodesy, Vol. 85, No. 3, March 2011, pp. 171–183, doi: 0.1007/s00190-010-0426-y.
“An Automated Editing Algorithm for GPS Data” by G. Blewitt in Geophysical Research Letters, Vol. 17, No. 3, March 1990, pp. 199–202, doi: 10.1029/GL017i003p00199.
Cycle Slips and the Ionosphere
“Improved Precise Point Positioning in the Presence of Ionospheric Scintillation” by X. Zhang, F. Guo and P. Zhou in GPS Solutions, Vol. 18, No. 1, Jan. 2014, pp. 51–60, doi: 10.1007/s10291-012-0309-1.
“Cycle Slip Detection and Repair for Undifferenced GPS Observations Under High Ionospheric Activity” by C. Cai, Z. Liu, P. Xia and W. Dai in GPS Solutions, Vol. 17, No. 2, April 2013, pp. 247–260, doi: 10.1007/s10291-012-0275-7.
“Mitigating the Impact of Ionospheric Cycle Slips in GNSS Observations” by S. Banville and R.B. Langley in Journal of Geodesy, Vol. 87, No. 2, Feb. 2013, pp. 179–193, doi: 10.1007/s00190-012-0604-1.
Real-Time Cycle-Slip Detection and Repair
“Real-Time Detection and Repair of Cycle Slips in Triple-Frequency GNSS Measurements” by Q. Zhao, B. Sun, Z. Dai, Z. Hu, C. Shi and J. Liu in GPS Solutions, Vol. 19, No. 3, July 2015, pp. 381–391, doi: 10.1007/s10291-014-0396-2.
“Real-Time Cycle Slip Detection in Triple-Frequency GNSS” by M.C. de Lacy, M. Reguzzoni and F. Sansò in GPS Solutions, Vol. 16, No. 3, July 2012, pp. 353–362, doi: 10.1007/s10291-011-0237-5.
The container shipping industry uses between 20–25 million containers, only a small number of which are tracked. A company called Traxens is on the verge of changing that.
In July, the Switzerland-based Mediterranean Shipping Company (MSC) joined worldwide container shipping company CMA CGM to invest in the French start-up. Under the deal, both CMA CGM and MSC will be represented on the board of directors of Traxens.
Traxens cargo tracker. (Photo: Traxens)
CMA CGM and MSC transport about 25 percent of the world’s shipping containers.
Established in 2012, Traxens has been developing solutions for the cargo logistics sector and has created a new multi-modal container monitoring and coordination system to provide real data for logistics.
By the last quarter of 2016, CMA CGM and MSC will have installed Traxens devices across their fleets.
“We see container monitoring as an important innovation in providing our customers with a high quality of service, while also being able to monitor our outputs accurately,” said MSC CEO Diego Aponte. “We believe that shipping lines should naturally compete on service, but should cooperate in the area of technology and innovation.”
“This should be the start of deployment on a massive scale,” said Tim Baker, Traxens director of marketing and communications.
CMA CGM, which has been backing Traxens since 2012, said that the investment is a part of its global digital strategy. Its 536 vessels call on more than 420 world ports. MSC operates an integrated network of road, rail and sea on more than 200 trade routes.
Each Traxens device has GPS on board, but other methods can be used to save battery life, which affects the overall cost of ownership of the solution. “For instance, once we have determined that a container is on board a ship, we can use the AIS ship-positioning data rather than the GPS on the device — especially as the device may be under deck with no view of the sky,” Baker said.
Also to save power, critical decisions on location are made by the devices locally rather than transmitting position up to the cloud and making decisions there. “It is much less power hungry to evaluate GPS position on the device, compare location with expected location, and then decide whether the information is worth transmitting than to send each position to the cloud just in case it happens to be interesting,” Baker explained.
Carlson Software has released the Carlson BRx6, a multi-GNSS, multi-frequency receiver. Each BRx6 contains a multi-constellation, multi-band 372-channel GNSS receiver, Athena RTK technology and an integrated Atlas L-band receiver.
In addition, the BRx6 contains electronic sensors that measure tilt, direction (electronic compass) and acceleration, supporting Carlson SurvCE’s advanced features such as LDL (live digital level or e-bubble), leveling tolerance, auto by level, tilted-pole correction and advanced stakeout features.
SurvCE contains sophisticated checks for compass and acceleration anomalies to ensure accuracy.
Designed for use by surveyors, contractors, builders and engineers, the Carlson BRx6 delivers the high positional accuracy at an affordable price.
Manufactured to Carlson’s exacting specifications by Hemisphere GNSS, the BRx6 provides robust performance and high precision in a compact and rugged package, Carlson said. With multiple wireless communication ports and an open GNSS interface, the BRx6 can be used as a precise base station or as a lightweight and easy-to-use rover.
The BRx6 receiver is powered by an Athena RTK (real-time kinematic) engine. RTK corrections can be received over UHF radio, cell modem, Wi-Fi, Bluetooth or serial connection.
The BRx6 also works as a base and rover with the new Carlson Listen-Listen cloud-based low latency RTK correction delivery service. The Carlson Listen-Listen service taps the built-in cell modem and reduces the need for UHF radio communication.
Multiple RTK rovers of any type can “talk to” a single BRx6 base by cell modem or Wi-Fi hot spot over extreme long distances. It reduces or eliminates dependency on VRS systems. Listen-Listen is provided on a free, 30-day trial basis with each BRx6 base and rover package purchased.
The BRx6 receiver can also be used with the subscription-based Atlas service, Hemisphere’s industry leading global correction service provided over L-band communication satellites and the internet.
When this service is included in an upcoming release of Carlson SurvCE, BRx6 users can achieve sub-decimeter positioning performance anywhere on earth, without the need for a fixed base station, a virtual reference network or other communication infrastructure.
The BRx6 can be purchased as either a rover or as a base/rover package. The base/Rover package includes two BRx6 GNSS receivers, two hard-sided carrying cases, four BRx6 batteries with two chargers, one GPS tribrach and one tribrach adapter, and two Carlson GPS receiver poles. The Rover package includes the BRx6 GNSS receiver, carrying case, two BRx6 batteries with charger, and cables. The BRx6 rover is available as a network rover (GSM cell modem only) or as a complete rover with UHF radio and GSM cell modem.
The Carlson BRx6 GNSS receiver is designed to work seamlessly with most data collectors including Carlson’s rugged and popular data collectors: the Carlson MINI2, the Carlson Surveyor2 and the Carlson RT3ruggedized tablet.
The first operational rules for routine commercial use of small unmanned aircraft systems, announced June 21, officially take effect today.
“People are captivated by the limitless possibilities unmanned aircraft offer, and they are already creating business opportunities in this exciting new field,” said U.S. Transportation Secretary Anthony Foxx. “These new rules are our latest step toward transforming aviation and society with this technology in very profound ways.”
“The FAA’s role is to set a flexible framework of safety without impeding innovation,” said Administrator Huerta, Federal Aviation Administration (FAA). “With these rules, we have created an environment in which emerging technology can be rapidly introduced while protecting the safety of the world’s busiest, most complex airspace.”
The provisions of the new rule — formally known as Part 107 — are designed to minimize risks to other aircraft and people and property on the ground. A summary is available.
Effective today, the FAA has several processes in place to help users take advantage of the rule:
Waivers. The agency is offering a process to waive some of the rule’s restrictions if an operator demonstrates the proposed flight will be conducted safely under a waiver. Users must apply for these waivers at the online portal.
The FAA is issuing more than 70 waivers today, based on petitions for Section 333 exemptions. These waivers will be posted on September1. The majority of the approved waivers were for night operations under Part 107.
Airspace Authorization. Users can operate their unmanned aircraft in Class G (uncontrolled) airspace without air traffic control permission. Operations in Class B, C, D and E airspace need air traffic approval. Users must request access to controlled airspace via the electronic portal at www.faa.gov/UAS.
The FAA will evaluate airspace authorization requests using a phased approach. Operators can submit their requests starting today, but air traffic facilities will receive approved authorizations, if granted, according to the following tentative schedule:
Class D & E Surface Area: Oct. 3, 2016
Class C: Oct. 31, 2016
Class B: Dec. 5, 2016
The FAA will make every effort to approve requests as soon as possible, according to the agency, but the actual processing time will vary, depending on the complexity of an individual request and the volume of applications the FAA receives. The agency is urging users to submit requests at least 90 days before they intend to fly in controlled airspace.
The FAA will use safety data from each phase to ensure appropriate mitigations are in place as small UAS operations are integrated into controlled airspace.
Aeronautical Knowledge Test. Testing centers nationwide can now administer the Aeronautical Knowledge Test required under Part 107. After an operator passes the test, he or she must complete an FAA Airman Certificate and/or Rating Application to receive a remote pilot certificate.
It may take up to 48 hours for the website to record that the applicant has passed the knowledge test. The FAA expects to validate applications within 10 days. Applicants will then receive instructions for printing a temporary airman certificate, which is good for 120 days. The FAA will mail a permanent Remote Pilot Certificate within 120 days.
In the future, the FAA also will address operations not covered by Part 107 without a waiver, including operations over people, beyond line of sight operations, extended operations, flight in urban areas, and flight at night.
Part 107 does not apply to model aircraft. Model aircraft operators must continue to satisfy all the criteria specified in Section 336 of Public Law 112-95 (which is now codified in part 101), including the stipulation they be operated only for hobby or recreational purposes. Click here for more information on hobby or recreation uses.
The U.S. Air Force is holding a 2016 Public Interface Control Working Group and Open Forum for the Navstar GPS public documents Sept. 21-22 in El Segundo, California.
The meeting is intended to update the public on GPS public document revisions and collect issues and comments for analysis and possible integration into future Navstar revisions.
The forum will be held for the following documents:
IS-GPS-200 (Navigation User Interfaces).
IS-GPS-705 (User Segment L5 Interfaces).
IS-GPS-800 (User Segment L1C Interface).
ICD-GPS-240 (Navstar GPS Control Segment to User Support Community Interfaces).
ICD-GPS-870 (Navstar GPS Control Segment to User Support Community Interfaces).
The 2016 Interface Control Working Group and Open Forum is open to the general public. The meeting will be held in the Great Room at 100 N. Sepulveda Blvd., El Segundo, California, 90245.
Those planning to attend should register by Sept. 7. To register, send the registration information to [email protected], providing your name, organization, telephone number, email address and country of citizenship.
Swedish-based survey and GIS equipment maker Satlab Geosolutions is offering a multi-purpose handheld that sends centimeter-level NMEA position data to the user’s tablet or smartphone.
The SLC RTK handheld brings professional high-precision positioning in a new design concept with Bluetooth connectivity for Android, Windows and iOS Bluetooth low-energy (BLE) smart devices, according to the company.
Alternatively, it can be used as a fixed sensor for any compatible NMEA driven positioning application.
The design includes a mounting plate to attach the user’s tablet device so it acts as the SLC’s display. Connectivity also is available via a USB/RS232 port. With a built-in wireless modem and optional remote antenna and pole- or fixed-mount accessories, the SLC can be configured as a sensor for machine control or other mobile applications.
SLC is flexible — it can be paired with data-collection software running on Windows, Android or iOS BLE with compatible applications. Its RTK positioning information can be used in numerous markets including land surveying, high-accuracy GIS, web-based facility management, utilities, pipelines, precise farming, hydrography, geophysics or aeronautics. With 32-GB internal memory, the SLC is also able to record RAW data to be used for post-processed applications.
The SLC has a built-in lithium ion battery and GNSS antenna for up to 12 hours of portable operation. It includes a Telit 3.5G GSM modem for operation as an RTK base or rover, transmitting or receiving corrections from NTRIP networks or via Satlab’s free Internet RTK service. Satlab Internet RTK allows users to stream corrections via IP to any of three Satlab servers around the world; any Satlab rover device can then connect to that same IP connection to receive full GNSS constellation corrections.
“Our new Scandinavian-designed SLC handheld is a different concept, offering RTK centimeter-level positioning at an incredible price in a flexible form factor,” commented Bjorn Agardh, CEO of Satlab. “With our simple SLC Toolbox software utility, users set up the SLC once, and it remains configured every time it’s used.”
The SLC comes in two configurations: as a handheld in a soft case with two tablet/panel mounting plates and a charging USB cable; or bundled with external geodetic antenna, cable and pole mount.
Q: Buyers get little guidance as to how specific receivers react to interference, particularly in critical infrastructure. Is there a need for receiver testing and certification along the lines of Underwriters Laboratories to guide purchase and acquisition?
Logan Scott, President, LSC
A: Exhaustive “seven-nines” testing and verification is expensive, takes a long time and stymies innovation. Yet simple and pragmatic testing can reveal faults very quickly. Numerous receivers fail to recognize that interference is occurring and/or produce hazardously misleading position with no warning to the user. Simple algorithms can detect problems quickly, and receivers should implement them. UL-style testing would reveal gross deficiencies in receivers and would provide a basis for selecting receivers.
Dana Goward, President, Resilient Navigation and Timing Foundation
A: Whether it’s a circular saw or a GNSS receiver, safe use of a tool requires understanding its capabilities and how to use it. I have heard all kinds of reports of the wrong type of receiver being used for critical applications. An authoritative process that clarifies receiver capabilities and appropriate use would greatly help buyers educate themselves. Ultimately, it would make us all safer.
Tony Murfin, Contributing Editor, Professional OEM & UAV, GPS World
A: Most high-end receiver manufacturers have worked for many years on GNSS interference resilience. Jamming incidents have pushed manufacturers harder for solutions because customers demand more. We don’t need legislation; market pressure alone continues to bring about better interference solutions. If you’re using a low-end receiver, it’s probably somewhat processor- and memory-constrained, so it’s hard to build in better signal processing. Time will inevitably fix this problem; in the meantime buy a better receiver.
A dense reference network facilitates low-cost carrier-phase differential GNSS positioning with rapid integer-ambiguity resolution. This could enable precise lane-keeping for automated vehicles in all weather conditions.
Strong demand for low-cost precise positioning exists in the mass market. Carrier-phase differential GNSS (CDGNSS) positioning, accurate to within a few centimeters even on a moving platform, would satisfy this demand were its cost significantly reduced. Low-cost CDGNSS would be a key enabler for many demanding consumer applications.
Centimeter-accurate positioning by CDGNSS has been perfected over the past two decades for applications in geodesy, precision agriculture, surveying and machine control. But mass-market adoption of this technology will demand much lower user cost — by a factor of 10 to 100 — yet still require rapid and accurate position fixing. To reduce cost, mass-market CDGNSS-capable receivers will have to make do with inexpensive, low-quality antennas whose multipath rejection and phase center stability are inferior to those of antennas typically used for CDGNSS.
Moreover, there will be a strong incentive to use single-frequency receivers, whereas almost all receivers used for CDGNSS in surveying and similar applications are multi-frequency. Despite these user-side disadvantages, mass-market precise positioning will be expected to demonstrate convergence and accuracy performance rivaling that of the most demanding current precise positioning applications: Users will be dissatisfied with techniques requiring more than a few tens of seconds to converge to a reliable sub-decimeter solution.
Meeting this challenge calls for innovation targeting both the rover (user) equipment and the reference network. Here we examine the challenge from the point of view of the reference network and offer demonstration results for a low-cost end-to-end system.
The recent trend in precise satellite-based positioning has been toward precise point positioning (PPP), whose primary virtue is the sparsity of its reference network. But standard PPP requires several tens of minutes or more to converge to a sub-10-centimeter 95 percent horizontal accuracy. Faster convergence can be achieved by recasting the PPP problem as one of relative positioning, thereby exposing integer ambiguities to the end user.
This technique, known as PPP-RTK or PPP-AR, is mathematically similar to traditional network real-time kinematic (NRTK) positioning. As the network density is increased, sub-minute or even instantaneous convergence is possible with dual-frequency high-quality receivers. Even single-frequency PPP-RTK is possible, with convergence times of approximately 5 minutes for a 40-kilometer network spacing.
For PPP-RTK and NRTK, convergence time is synonymous with the time required to resolve the integer ambiguities that arise in double-difference (DD) carrier-phase measurements, referred to here as time to ambiguity resolution, or TAR. As reference networks become denser, they can better compensate for spatially-correlated variations in signal delay introduced by irregularities in the ionosphere and, to a lesser extent, in the neutral atmosphere. Improvement is manifest as reduced uncertainty in the atmospheric corrections that the network sends to the user. Reduced uncertainty in the atmospheric corrections is key to reducing TAR.
Prior work has established an analytical connection between uncertainty in the ionospheric corrections (denoted σi ) and TAR. The existing literature does not, however, offer a satisfactory model for the dependence of σι on network density.
The prevailing model is based on single-baseline CDGNSS, which is inapt for PPP-RTK and NRTK. Moreover, prior work does not address the effect of network-side multipath on the accuracy of the corrections data, which becomes increasingly important as low-cost and poorly-sited reference stations are used to densify the network.
Here, we examine the relationship between ionospheric uncertainty and probability of correct ambiguity resolution, and present the results of an empirical investigation of the relationship between network density and the total uncertainty in network correction data. We developed a simple analytical model relating error variance in network corrections to network density. Our analysis and experiments indicate that for rapid TAR in challenging urban environments with low-cost receivers, network density must be significantly increased. We report on the design and deployment of a dense network in Austin, Texas, and demonstrate a new system that taps into the network to provide reliable vehicle lane-departure warning.
AMBIGUITY RESOLUTION
Reducing the ionospheric uncertainty σι allows a strong prior constraint to be applied in the ionosphere-weighted model, thereby increasing P( = z), the probability that the estimated and true integer ambiguity vectors are equivalent. It is instructive to consider single-epoch ambiguity resolution (AR), for two reasons.
First, for stationary users with low-cost equipment, multipath errors dominate in the carrier-phase measurement and are strongly correlated over 100 seconds or more. Thus, if single-epoch AR fails then a static user may have to wait an unacceptably long time for multipath errors to decorrelate enough to permit AR. In any case, singe-epoch performance is a strong predictor of multi-epoch performance over an interval short enough (a few tens of seconds) to satisfy impatient mass-market users.
Second, a convenient and accurate analytical model (by Dennis Odijk and PJG Teunissen) for single-epoch AR reveals the dependency of P( = z) on scenario parameters of practical interest: the standard deviation of ionospheric correction errors, the number of visible satellites, the standard deviation of undifferenced carrier- and code-phase measurement errors (including multipath-induced errors), a satellite geometry factor, the number p of free parameters to be estimated (p=3 for negligible tropospheric error, p=4 to estimate a single additional tropospheric parameter), and the number of carrier frequencies broadcast by each of the satellites (1, 2 or 3) along with each carrier’s wavelength.
The model is highly accurate for single-epoch AR, but only approximate for multiple epochs, with accuracy degrading as the data interval lengthens. The model’s inaccuracy results from its assumption that overhead satellites remain static from epoch to epoch, which yields pessimistic results for even fairly short data capture intervals (for example, 30 seconds). Fully accounting for satellite motion in an analytical model for P( = z) is an open problem, which is why studies that wish to account for satellite motion resort to simulation.
Figures 1 and 2 show single-epoch, single-frequency results from the analytical P( = z) model for parameters approximately reflecting the mass-market use case. The most important conclusion to draw from these figures is that for single-epoch, single-frequency AR to be even moderately reliable (PT⩾0.9) over the next few years, the ionospheric uncertainty σι must be held under 2 millimeters. This will relax somewhat as more Galileo and MEO BeiDou satellites come online, but signal blockage in built-up areas will raise the effective elevation mask angle significantly above the 15 degrees assumed here, reducing the number of available satellites. Thus, sub-2-mm ionospheric uncertainty remains desirable for urban environments even as GNSS constellations become fully populated.
Figure 1. Single-epoch single-frequency ambiguity fixing. Blue traces (left axis) indicate the probability P(z^=z) of correctly resolving all integer ambiguities with a single epoch of data as a function of the number of satellites m. Each trace represents P(z^=z) for a different value of ionospheric uncertainty σι. Green bars (right axis) represent the probability mass function P(m) for the number of satellites above an elevation mask angle of 15 degrees, assuming 31 GPS, 14 Galileo, and 3 WAAS satellites (projected mid 2017). Each blue trace is marked with the total probability of correct integer resolution PT, a function of both the trace itself and P(m). Other parameters of the scenario: geometry factor fg=2.5, standard deviation of undifferenced phase measurements σϕ=3mm, standard deviation of undifferenced pseudorange measurements σρ=50cm, and number of estimated parameters p=3.Figure 2 . Total probability of a correct fix for the scenario of Figure 1 as a function of ionospheric uncertainty σι.
Figures 3 and 4 offer results for a dual-frequency (L1-L2) single-epoch scenario. All other scenario parameters are held as for the single-frequency scenario except that, in an attempt to be somewhat more pessimistic, P(m) is based only on GPS satellites. It is assumed that from each satellite the user can extract dual-frequency measurements. As with the single-frequency case, it is evident that dual-frequency PT is strongly dependent on σι. The dual-frequency case is more forgiving, but substantial performance improvement can still be had by reducing σι to under 2 mm.
Figure 3. As Figure 1 except for dual-frequency (L1-L2) measurements and the probability mass function P(m) corresponds only to a constellation of 31 GPS satellites. The elevation mask angle is again taken to be 15 degrees. It is assumed that dual-frequency measurements can be obtained from every GPS satellite.Figure 4. Total probability of a correct fix for the scenario of Figure 3 as a function of ionospheric uncertainty σι.
Corrections Uncertainty and Network Density. A key question arises in connection with σi: How is related to reference network density? One expects to decrease with increased network density, but what is the exact relationship?
Dennis Odijk’s work adopts a linear relationship between σi and the distance l between the user and the nearest reference station:
σi = βl, 0.3 ≤ β ≤ 3 mm/km
Parameter β depends on ionospheric activity; Odijk recommends determining β empirically. Similarly, his other work adopts a linear relation, with β = mm/km. But there appears to be no justification for applying this linear model to ionospheric corrections provided to a user by a network of reference receivers. The linear trend corresponds to individual single-baseline solutions involving a single master reference station without network aiding; it is not representative of how σi varies for a rover within a reference network.
Instead of determining how σi varies throughout a reference network, it will be more useful to consider the spatial variation in the variance of aggregate error in network-provided corrections. The aggregate error variance, denoted , can be modeled as the sum of variances associated with (1) residual ionospheric delay error, (2) residual neutral atmospheric (hereafter tropospheric) error, and (3) error due to carrier-phase multipath at the reference network stations:
This model assumes that precise orbital ephemerides are used to eliminate spatially-correlated errors due to satellite ephemeris errors and that the contribution to from reference station carrier-phase thermal noise is negligible compared to reference station carrier-phase multipath error.
Focusing therefore on σv , consider its relationship to reference network density γ, expressed in stations per unit area. This relationship depends on the assumed model for the DD ionospheric and tropospheric delays. Let a denote the master reference station and let S = {s1, s2, …, sN} denote the set of all secondary stations available in the network. Then, for pivot satellite i and alternate satellite j , suppose that the true combined DD atmospheric delay at secondary station s∈S can be accurately modeled as follows, where xs, ys, and zs represent the secondary station’s east, north, and up displacement from the master: (1)
Dai et al. refer to this model as a linear interpolation model or first-order surface model. The quantities and are the model parameters for the satellite pair i, j.
Map showing trends in σv across a simulated reference network assuming a linear model for combined DD ionospheric and tropospheric delays and independent errors due to multipath at each station. The master station is marked in black; secondary reference stations are marked in white. Blue denotes low σv. Red denotes high σv.
For the linear model in (1), one can show that if stations are sufficiently uniformly distributed (i.e., no station clumping), then the average value of σv across a network, denoted , is approximately related to the network density γ by (3)
where q is a parameter related to the variance of the uncorrelated errors , s∈S. This approximation becomes highly accurate as γ increases. [See full paper for details.]
It is clear from (3) that, for the linear model (1),can be driven to an arbitrarily small value by increasing the network density γ, and this is true despite the presence of multipath in the reference station carrier-phase measurements. Whether (3) applies in practice depends on whether (1) can be considered an accurate model for , at least over a compact region. The following section examines this question empirically. It further seeks to identify, for an example dense reference network, the density γ beyond which further reduction inno longer matters (would no longer improve ) because rover multipath dominates.
ANALYSIS OF A DENSE REFERENCE NETWORK
We examined σι as a function of network density using data from several organizations providing GNSS reference station observations: National Geodetic Survey Continuously Operating Reference Stations, UNAVCO, and the California Real Time Network. This combination allowed analysis of a hypothetical reference network of 23 high-quality GNSS receivers with an overall network density of approximately 8 nodes/1,000 km2, or an average inter-station spacing of 14 km. The relative positions of the sites selected to comprise this reference network, located between Los Angeles and Pomona, California, are depicted graphically below.
Depiction of the placement of the 23 GNSS reference stations. Horizontal positions are relative to the master station, LONG of CRTN, in kilometers. The color map indicates the height of each station above the WGS 84 geoid in meters.
DD carrier-phase observations from GPS L1 C/A signals spanning GPS weeks 1850 through 1859 were used for the analysis. A minimum satellite elevation mask was enforced at 20 degrees. Any satellite not above the elevation mask and providing carrier-phase observations at both the beginning and end of each processing window was excluded. A step size of 10 minutes was used. The longest available sub-window, meeting the above requirements and providing a minimum of 6 satellite vehicles (1 pivot satellite and 5 others), was selected for processing.
To facilitate batch processing, integer ambiguities were assumed to be resolved correctly when the mean standard deviation of carrier-phase residuals for that solution was less than one quarter wavelength of the GPS L1 frequency. In application, this constraint resulted in rejecting only 0.6 percent of all solutions.
Network Corrections Estimation. Estimation of network corrections made use of least-squares estimation applied to carrier-phase residuals measured between master station LONG, denoted a hereafter, and secondary reference stations s∈S, where S is now taken to be the set of all stations other than LONG. Consider the following model for the DD carrier-phase measurement, expressed in meters, between master station a, secondary station s∈S, pivot satellite i, and alternate satellite j:
(4)
Here, λ is the carrier wavelength; is the DD carrier-phase measurement, in cycles; is the DD range; is the DD integer ambiguity; is the DD combined atmospheric delay, which includes tropospheric and ionospheric delays; and is the DD carrier-phase measurement error, which is dominated by carrier-phase multipath error at a and s.
Experimental analysis ofas a function of network density proceeded as follows. A subset of secondary stations Sk ⊂S was chosen, together with a, to act as the kth test network. A large number K of subsets Sk of various geographic size and density were analyzed. Let {S\Sk} denote the set of secondary stations not in the kth test network. For each Sk, k = 1, 2,…, K, all secondary stations in {S\Sk} were designated, one at a time, to act as a test station, or rover. Atmospheric delays estimated by the kth network for test station s∈{S\Sk} were then differenced from actual delays measured by s to evaluate the quality of the atmospheric delay estimates.
Details of the atmospheric delay estimation procedure for the kth test network are as follows. For each s∈Sk, a DD measurement residual was formed for each pivot satellite i and alternate satellite j as
(5)
where was assumed known to sub-millimeter accuracy and was assumed to have been resolved correctly. The true DD atmospheric error contributing to (5) was assumed to vary linearly with geometry over sufficiently short baselines as modeled in (2). The DD multipath error term was assumed to be zero mean, and the component due solely to s was assumed to be uncorrelated with all corresponding components .
Under these assumptions, can readily be estimated via least squares. Let be the vector containing the residuals for |Sk|x1. This residuals vector can be modeled as
(6)
where H is an |Sk|x4 matrix whose rows are of the form [xs ys zs1]. The 4 x 1 vector contains the parameters of the hyper-plane to be estimated at each epoch. The |Sk|x1 vector wijcontains DD measurement errors.
An estimate from a least-squares solution of (6) was used to produce a network correction for a test secondary station s∈{S\Sk}, acting as rover, at location xs, ys, zs :
(7)
The subscript l on the atmospheric correction indicates that the correction is based on a linear model for DD atmospheric errors; it is used to distinguish the correction from those produced by a quadratic model later on. The correction was applied at test station s∈{S\Sk} to produce a corrected DD phase measurement
This procedure was repeated at each epoch for each satellite pair i, j visible to each test station s∈{S\Sk} of the kth test network, k = 1, 2,…K.
If the assumed models hold, then in the limit as the network density increases, can be modeled as
(8)
where is DD phase measurement error due only to multipath at s. In other words, as network density increases, application of the network correction eliminates not onlybut also , the component of the DD phase measurement error due to multipath at the master.
Linear least-squares compared to quadratic-least squares estimation. To evaluate the assumption that DD tropospheric and ionospheric errors vary proportional to relative position, c1 was estimated with the full set of secondary stations S for single epochs at 300 second intervals. The probability distributions of the contributions of those parameters (e.g., cxlxs and not simply cxl) are shown below. For comparison, equivalent values are calculated for a quadratic least-squares estimate of the following form:
(9)
Here, the subscript q of denotes a quadratic model for DD atmospheric delays. The distributions of comparable terms from (9) are also shown in the next two figures. These data represent the collection of all satellites above the elevation mask angle. It is noted that when all satellites are considered together, the expected value of these terms is very near zero.
Probability densities of the terms estimated at the station location for SPMS of UNAVCO. As indicated by the legend, the linear components are shown for a linear least-squares estimation as well as the linear components for a quadratic least-squares estimation. These data represent the probability densities for all GPS satellites combined.
Probability densities of the terms calculated at the station location for SPMS of UNAVCO.
The next two figures show the same data as the two above, but with each GPS satellite plotted separately. It is noted that the linear parameters, when considering only a particular satellite, are not necessarily zero-mean. This is hypothesized to be a manifestation of the satellite orbit reflected in the tropospheric and ionospheric errors. It is interesting to note that the quadratic terms shown in the second figure below largely exhibit zero mean behavior despite non-zero mean for the associated linear terms.
Probability densities of the terms for every GPS satellite observed, calculated at the station location for SPMS of UNAVCO, where each plot line represents a different GPS satellite. This figure is intended to qualitatively illustrate the non-zero mean nature of these linear terms when considered for individual satellites.
Probability densities of the terms for every GPS satellite observed, calculated at the station location for SPMS of UNAVCO, where each plot line represents a different GPS satellite. This figure is included to qualitatively illustrate the largely zero mean nature of these quadratic terms when considered for individual satellites.Probability densities of the difference between linear least-squares and quadratic least-squares network correction estimates for representative reference stations. The red vertical lines denote boundaries between which 68.27% of the probability distribution is contained; displayed as a comparative proxy to lσ of the Gaussian-distribution (these distributions are non-Gaussian). Recall that CGDM has a distance to the master station of 15.1km, BGIS is at 21.6km, and LORS is at 23.1km.
The figure above shows the probability distributions of the difference between (7) and (9) (i.e., ) at three representative reference station positions. It can be noticed that despite the increasing baseline distance of LORS and BGIS as compared to CGDM, there is no apparent correlation in these estimation errors. Notice that CGDM and LORS have very similar distributions despite their difference in baselines. BGIS and LORS, with similar baselines, exhibit very different distributions. There is no apparent correlation found between reference station positions and these error terms. Additionally, these distributions are zero-mean for all s∈S (to within 0.5 mm in each case) with 68.27% boundaries positioned between 1.5-5.5 mm. Because these errors appear indistinguishable from multipath, it is concluded, for this specific network and time period, that linear least-squares estimation is sufficient for estimating tropospheric and ionospheric errors. This is fortunate, because the linear model for atmospheric DD delays provides an averaging effect on multipath present at the reference stations which minimizes the introduction of multipath errors into the estimates produced.
Uncorrected carrier-phase residuals. The figure below shows the expected values for DD carrier-phase residual standard deviations for all s∈S through use of uncorrected observations. These data were produced by averaging the standard deviation of the DD carrier-phase residuals calculated at each epoch across all satellites present in the solution. The fitted curve indicates a linear growth of DD carrier-phase residuals with β = 0.62 mm/km. Additionally, the mm-level scatter of these data points suggest that position biases of the resolved reference station positions are also mm-level. If the linear fit is shifted down by approximately 4 mm (e.g., taking the minimum data points as those with very little position bias) and extrapolated to 0 km, one can consider this as providing a rough estimate of DD multipath at the reference stations; 4.7 mm (DD) or 3.3 mm (single-difference equivalent).
Standard deviation of uncorrected DD carrier-phase residuals versus baseline distance between each of the 22 reference stations and the master reference station.
Uncorrected Carrier-Phase Residuals. Figure 5 shows the expected values for DD carrier-phase residual standard deviations for all secondary stations, based on observations that were not corrected for atmospheric delay. These data were produced by averaging the standard deviation of the DD carrier-phase residuals calculated at each epoch across all satellites present in the solution. The fitted curve indicates a linear growth of DD carrier-phase residuals with distance to the master. The mm-level scatter of these data points suggest that biases of the resolved reference station positions are also mm-level.
Figure 5. Standard deviation of uncorrected DD carrier-phase residuals versus baseline distance between each of the 22 reference stations and the master reference station.
Network-Corrected Residuals. Figure 6 displays similar data to Figure 5, except that the carrier-phase residuals are those that remain after network corrections are applied. Each data point corresponds to a particular subset of secondary stations together with the master, and a particular rover selected at random from the remaining stations. Both the size and specific selection of secondary stations comprising each subset were randomly selected. In all, 70 different network configurations and more than 3.67 million NRTK solutions were analyzed.
Figure 6. Standard deviation of carrier-phase residual remainders (the carrier-phase residuals which remain after application of network corrections) versus average network density. The fitted curve is simply a polynomial fit of these data; it is not based on any theoretically anticipated behavior.
Figure 6 shows that carrier-phase residuals after application of network corrections are considerably reduced compared to those original magnitudes seen in Figure 5. With increasing network density, the DD residuals’ deviation asymptotically approaches a minimum value of about 4 mm, which corresponds to an undifferenced deviation of 2 mm. This floor is due to multipath at the rover. Deviations in excess of this floor are caused by residual ionospheric errors and, to a lesser extent, neutral atmospheric errors.Attributing the excess deviation entirely to residual ionospheric errors, and assuming these are uncorrelated with multipath, one can estimate from Figure 6 the undifferenced ionospheric uncertainty. For example, for a 50-km inter-station distance, σι=(℘(142 – 42))/2=6.7mm. To achieve the σι<2 mm recommended earlier for fast and reliable AR, station separation should be no more than 22 km, which we round down to a recommended value of 20 km to provide a margin of station redundancy.
NETWORK DEPLOYMENT
We have developed and deployed a low-cost reference network testbed in Austin, Texas, with site hosting courtesy of the Texas Department of Transportation. The Longhorn Reference Network boasts a dozen stations, with plans for 20 (Figure 7). The network’s average inter-station spacing is far shorter than the 20-km spacing recommended earlier. The tighter spacing provides redundancy and flexibility of experimentation. The low-cost reference stations are deployed in environments with greater multipath and signal blockage than those of the high-quality stations studied earlier. Such non-ideal signal environments are to be expected in a dense low-cost reference network, for which choice of station siting is driven largely by opportunity.
Figure 7. Overview of the planned Austin area reference network (Google Maps).
The reference station design, pictured in Figure 8 and diagrammed in Figure 9, is novel. Each station is a self-contained, solar-powered node supporting a software-defined dual-frequency, dual-antenna GNSS receiver with an always-on cellular connection to university servers for data collection and software maintenance.
Figure 8. Low-cost reference station in the Longhorn Reference Network.Figure 9. Reference station components.
Live Vehicle Demonstration. In partnership with Radiosense, an Austin-based precise positioning startup, we have developed and demonstrated a low-cost vehicle lane departure warning system that receives corrections from our dense reference network. The system takes in lane widths from an external database and infers a safe driving corridor within each lane by analyzing the behavior of human drivers on the same road. A vehicle’s proximity to the lane boundary is displayed in real time to the driver and passengers.
For robustness against cycle slips and to provide a baseline against which to compare future improvements, the system currently employs single-epoch CDGNSS positioning without aiding from additional sensors. In choosing a single-epoch approach, the system naively discards information regarding the underlying integer ambiguities at the beginning of each measurement epoch. Still, the system performs well with the typical number of overhead signals in a light urban environment: correct and internally-validated solutions were available in over 92 percent of measurement epochs. When a second rover antenna is included to combat multipath with spatial diversity, this percentage improves to 96. Such good single-epoch performance suggests that, when armed with additional sensor aiding and proper integer ambiguity persistence, reliable and accurate vehicle positioning can be maintained in more challenging environments.
Demonstration setup. The live demonstration followed a predetermined route in the vicinity of the University of Texas campus. The 1-mile route (Figure 10) passed through both open-sky and partially-blocked environments.
Figure 10. Demonstration route.
Prior to the demonstration, the vehicle was driven several times on the same route collecting GNSS measurements to precisely map typical driving trajectories on the route. The ensemble of trajectories was used to build a centimeter-accurate model of the lane center along the route. The sensing equipment employed during this mapping phase is no different than that used during the demonstration, making feasible eventual crowd-sourcing, wherein end-user vehicles generate and update the centerline models.
The demonstration vehicle was outfitted with two dual-frequency GNSS antennas mounted with magnetic bases onto the roof. The first antenna, designated primary, operated as the rover in a single-baseline CDGNSS solution against the master reference station of the Longhorn Reference Network, as illustrated in Figure 11. This baseline provided the geo-referenced, centimeter-accurate vehicle position. The other antenna, designated secondary, was paired with the primary antenna to produce a constrained-baseline CDGNSS solution providing sub-degree-accurate vehicle heading. The secondary antenna also served as a backup when the primary antenna produced a result that did not pass the precise positioning engine’s internal validity testing.
Figure 11. GNSS antenna configuration. A single-baseline precise position solution between the primary antenna and the master reference station provides precise vehicle position. A constrained-baseline 2D attitude solution between the primary and secondary antennas provides heading.
The GNSS antennas were connected to a low-cost, dual-frequency front-end in the trunk of the vehicle (FIGURE 12)which downconverted and digitized the incoming signals and subsequently fed them to a low-cost single-board computer running the precise positioning engine. A cellular modem received real-time measurements from the master reference station, while a WiFi router streamed real-time solutions to several Android devices in the vehicle for real-time visualization of precise within-lane position.
Figure 12. Low-cost, dual-frequency rover system in the trunk of the vehicle.
Demonstration Results. Figures 13, 14 and 15 show snapshots of the Android application and a still frame of the side of the vehicle in three different scenarios. The large rectangle indicates vehicle position with respect to the modeled lane center, changing color from green, when the vehicle is within the safe driving corridor, to yellow as the vehicle nears the edge of the lane, and finally to red if the vehicle breaks the lane boundary. One could imagine wrapping a control loop around these signals to enable last-moment lane-keeping.
Figure 13. Vehicle position relative to lane edge (left) synchronized in time with video still frame (right), centered safely within the lane, as depicted by green rectangle.Figure 14. Vehicle nearing lane edge, as depicted by yellow rectangle.Figure 15. Vehicle crossing lane edge, as depicted by red rectangle.
Figure 16 reveals the precision with which the positioning engine was able to locate the vehicle’s driver-side antenna in four repeated passes along the test route. The variation between the four yellow traces is primarily due to driver non-repeatability; actual measurement precision is at the centimeter scale. A small bias in the traces’ registration to the picture is present because Google Earth imagery is only registered to the International Terrestrial Reference Frame with meter-level accuracy.
Figure 16. Four repeated traces of driver’s side antenna as vehicle made a turn.
Figure 17 shows a time history of the vertical deviation from the route mean, in meters. The zoomed view of the vertical deviation shown in Figure 18 allows one to appreciate the precision of the positioning engine: the vertical trajectory is smooth at the centimeter level. Figure 19 shows the DD residuals in carrier phase and pseudorange for GPS PRN 30 during the four loops in Figure 17. One-sigma undifferenced phase and pseudorange deviations are 3.4 mm and 42 cm, respectively.
Figure 17. Time history of the vertical deviation from the route mean, in meters.Figure 18. Zoomed view of the time history of the vertical deviation from the route mean, showing the centimeter-level precision in the 3.3 Hz positioning data.Figure 19. Double-difference carrier phase (top) and pseudorange (bottom) residuals for GPS satellite 30 at frequency L1 over the full time interval shown in Figure 17.
The figures demonstrate that the precise positioning engine fed by reference data from the Longhorn Reference Network maintained centimeter-accurate knowledge of the vehicle’s position during almost the entire trajectory, despite passing between a large football stadium and parking garage, each of which introduced significant signal blockage and multipath.
For the data shown in Figure 17, 96 percent of the 3.3-Hz measurement epochs resulted in a correct and internally-validated positioning solution. The majority of the remaining solutions were correct but did not pass internal validation. For only 0.6 percent of solutions were the carrier-phase integer ambiguities resolved incorrectly, but all of these incorrect solutions were caught and excluded by the validation algorithm.
Furthermore, the number of overhead signals during the time in which this particular dataset (set A) was taken was average, as seen in the upper plot of Figure 20. 16 signals above 15 degrees elevation were available during this time. In contrast, the number of overhead signals for a second dataset taken 8 days prior (set B) was much worse, with only 12 signals above 15 degrees elevation, as seen in the lower plot.
Figure 20. The number of signals above a 15-degree elevation mask. Each plot spans an entire day. The black arrows denote the time of day in which two different datasets, A and B, were taken. The dashed red line represents the mean number of signals above the mask over both days. Dataset A was taken during a nominal time when 16 signals were available, while dataset B was taken during a worst-case time when only 12 signals were available.
For insight into the performance of the positioning engine as a function of the number of overhead satellites, Table 1 details the performance of these two datasets (as well as a third dataset) in terms of the percentage of epochs that passed the positioning engine’s internal validation testing, based on a ratio test with a fixed threshold of 2.0. Results are shown for single- and dual-antenna positioning solutions and for dual-antenna vehicle heading solutions.
Table 1. The performance of each dataset in terms of the percentage of solutions that passed validation testing.
A large drop-off in positioning performance occurs when the number of overhead signals is reduced below 16, while the constrained-baseline heading determination performance remains good throughout. Fortunately, it will not be long until even more signals are available. Within the next 8 months, the Galileo constellation will add six fully operational satellites. These will bring the number of GPS L1, GPS L2C, Galileo E1, and SBAS signals that are above 15 degrees elevation to 16 or more 95 percent of the time, enabling high-reliability single-epoch CDGNSS positioning.
CONCLUSIONS
For a sufficiently dense reference network, linear least squares estimation can be applied to the task of reducing uncertainties due to tropospheric and ionospheric delays for the purposes of providing improved positioning accuracy as well as faster time to ambiguity resolution for carrier-phase differential positioning. High network density allows use of a strong linear model for atmospheric delays, which has the virtue of suppressing network-side multipath errors in the provided corrections.
A network of 23 high-quality reference stations in the vicinity of Los Angeles, California, was studied to determine what network density is sufficient to make all network-side error sources negligible compared to rover receiver multipath. A density of three stations per 1,000km2, or an average inter-station spacing of 20 km, was found to drive network-side ionospheric, tropospheric, and multipath errors well below rover receiver multipath.
These findings motivate a significant densification of permanent reference networks, at least in built-up areas where signal blockage and multipath are common, to support mass-market applications for which low user (rover receiver) cost and rapid convergence to a reliable sub-decimeter position are a priority. In a light urban setting, and with the kind of satellite coverage that will soon become the norm, we demonstrated vehicle lane departure warning in a field test that produced highly reliable instantaneous sub-decimeter positioning.
ACKNOWLEDGMENTS
This work was supported in part by Samsung Research America, by the Data-Supported Transportation Operations and Planning Center (D-STOP), a Tier 1 USDOT University Transportation Center, and by the Texas Department of Transportation under the Connected Vehicle Problems, Challenges and Major Technologies project.
Dual Mode Plus uses inertial guidance with GPS updates to shape flight path for target engagement at desired impact heading and dive angle. (Photo: Lockheed Martin)
Lockheed Martin’s new Dual Mode Plus laser guided bomb (LGB) successfully completed two recent flight tests at the Naval Air Warfare Center Weapons Division in China Lake, California.
The tests demonstrated operation of the new linear optics, GPS/inertial navigation system (INS) guidance subsystem and the control actuation system, meeting all mission objectives.
Released from an F/A-18 Super Hornet, the two Mk-82 (500-lb.) inert warheads, fitted with Dual Mode Plus guidance kits, impacted fixed targets well within operational performance requirements.
“Lockheed Martin’s Dual Mode Plus benefits from the reliability and affordability of the Paveway II Plus LGB system while integrating a GPS/INS, all-weather moving target capability,” said Joe Serra, Precision Guided Systems director at Lockheed Martin Missiles and Fire Control. “This combination offers a precise and affordable direct attack weapon system to the U.S. and its allies.”
Effective against fixed, relocatable and moving targets, Dual Mode Plus will improve mission effectiveness by providing precision strike capabilities in all-weather conditions at extended standoff ranges.
Dual Mode Plus maintains Paveway II LGB physical dimensions and easily integrates with aircraft employing Paveway II LGBs or other similar direct attack weapons utilizing conventional MIL-STD-1760/1553 or Universal Armament Interfaces.
Lockheed Martin is a qualified provider of all three Paveway II MK-80 series LGB variants (GBU-10 MK-84 [2,000 lb.], GBU-12 MK-82 and GBU-16 MK-83 [1,000 lb.]) and is the sole provider of the Enhanced Laser Guided Training Round and Dual Mode LGB kits.
The company has delivered more than 150,000 training rounds, more than 75,000 Paveway II LGB kits and 7,000 dual-mode systems to the U.S. Navy, Marine Corps, Air Force and 23 international customers.
The Federal Aviation Administration’s (FAA) new small drone rule — formally known as Part 107 — is effective on Monday, Aug. 29. The FAA has released information to help drone users understand the new requirements.
Below is information on Part 107’s effect on Section 333 waivers, along with how to obtain a Part 107 waiver.
Section 333 vs. Part 107: What works for you?
The biggest question is whether you are better off flying under the provisions of Part 107, or should continue using your existing exemption? The video below explains what happens to your Section 333 exemption grant or petition for exemption.
Your exemption is valid until it expires — usually two years after it was issued. Even after Part 107 becomes effective, you may choose to fly following the conditions and limitations in your exemption.
However, if you want to operate under the new Part 107 regulations, you’ll have to obtain a remote pilot certificate and follow all of the rule’s operating provisions. You must apply for a waiver if some parts of your operation don’t meet the rule’s requirements.
If you already have a Certificate of Waiver or Authorization (COA) under your Section 333 exemption, you can continue to fly under the COA limitations until it expires. If you don’t already have a COA, you probably won’t need one when the new drone rules go into effect.
However, if you want to fly in controlled airspace, you will need permission from FAA air traffic control. Details about obtaining that permission will be online at www.faa.gov/uas when the small drone rule is effective on Aug. 29.
If you applied for a Section 333 exemption but haven’t received it yet, you should have received a letter from the FAA with specific information about the status of your petition. Generally, if your petition is pending and falls within the provisions of the rule, you should follow the steps outlined in the rule.
Whether you choose to fly under your exemption or under the new small drone rule is your choice, depending on how you want to operate your aircraft. You’ll have to compare the conditions and limitations in your exemption to the operating requirements in the rule to determine which one best addresses your needs.
Applying for a waiver under the new drone rules
Part 107 allows for some expanded operations based on technology mitigations if you can make the safety case for a waiver of some provisions. Operators can apply for waivers to operate at night, beyond line of sight, above 400 feet and other specific types of operation.
Here’s what you need to know about the waiver process:
Under Part 107, you may request a waiver of certain provisions starting Aug. 29 if your operations don’t quite fit under the rule’s provisions. On Aug. 29, the FAA will have an online portal you can use to request waivers of applicable Part 107 regulations at www.faa.gov/uas.
The FAA won’t grant waivers automatically, and processing your waiver request may take time. The exact length of time will depend on the volume of requests the agency receives and the complexity of the waiver application. You should submit your waiver requests to the FAA as early as possible, at least 90 days before you plan to fly.
If you have a Section 333 exemption grant, and we previously said you could operate under Part 107 with a waiver, you will receive a letter notifying you that we have granted you a 0waiver or that we need additional information for you to make your safety case. (See above section.)
Information on the regulations potentially eligible for a waiver is here. Below is a short video on the waiver process.
Sensonor AS is partnering with NASA to supply current and future low- and near-Earth orbit space missions with inertial and gyroscope modules.
The Norway-based company first began supplying its standard inertial measurement unit (IMU) and gyroscope modules for low Earth orbit (LEO) space applications in 2012, beginning with the launch of the NASA-sponsored AeroCube-4 satellite. Sensonor’s STIM300 and STIM210 inertial products are now a standard part in many spacecraft similar to the AeroCube-4.
Current NASA projects using STIM inertial systems include the Raven technology demonstration and Near Earth Asteroid (NEA) Scout.
Raven, which launches to the International Space Station in September, will test key elements of an autonomous relative navigation system. Its technologies may one day help future robotic spacecraft autonomously and seamlessly rendezvous with other objects in motion, such as a satellite in need of fuel or a tumbling asteroid.
The concept image above shows the NEA Scout CubeSat with its solar sail deployed as it characterizes a near-Earth asteroid. (NASA)
The NEA Scout is a robotic reconnaissance mission that will be deployed to fly by and return data from an asteroid representative of NEAs.
NASA, in conjunction with the Aerospace Corp., spearheaded the use of STIM products in space, and many other commercial launch and satellite companies have since followed NASA’s lead. In fact, more than 30 companies around the world use Sensonor inertial products in various space applications, with several satellites successfully flying with STIM gyroscope modules for over three years.
The STIM gyroscope modules are often used in combination with GPS or a Star Tracker and Kalman Filter to orient and stabilize the satellite, as well as to provide feedback on satellite motion induced by its reaction wheels. In some applications, the gyroscopes are used to stabilize satellite- to-satellite communications.
Being a supplier illustrates the trust NASA and others place in Sensonor, further solidifying the company’s role in this market. “We look forward to continuing to serve the international space community with our inertial offerings as standard commercial off-the-shelf (COTS) products. By serving the space market on equal terms with our other customers, we can help to reduce the cost of manufacturing and launching space payloads,” said Hans-Richard Petersen, Sensonor’s vice president of sales and marketing. “Our STIM products are the lowest size, weight, and power for their performance level in the market, with 5 to 10 times lower weight than the next-best alternative with similar performance. This makes them a very cost-effective and attractive solution.”
Sensonor will continue to improve its gyroscope module and IMU product performance and features, and is actively working with the space community to enhance its standard commercial-off-the-shelf (COTS) parts. Following the tremendous interest from the space community, Sensonor has initiated a space-optimized version of its STIM gyro module.
