Oscilloquartz has launched the OSA 5405 SyncReach, an integrated PTP grandmaster and GNSS receiver with a patent-pending dual antenna and receiver to enable the mass roll out of small cells.
The new technology has been specifically engineered to provide accurate and affordable phase synchronization for the rapidly growing small-cell market and meet the stringent timing requirements of 4.5G and 5G connectivity.
With the OSA 5405, operators can migrate from legacy GNSS RF antennas and cables to standard, cost-effective copper and fiber Ethernet cabling, reducing capital expenditure and operating expenses, Oscilloquartz said.
Available in both indoor and outdoor variants, the OSA 5405 can be deployed in challenging environments, including urban canyons where GPS signals fail. The OSA 5405’s miniscule form factor also enables it to be positioned on indoor windows to avoid multipath signal interference from objects within the building.
The OSA 5405 uses a unique dual GNSS antenna and receiver algorithm to mitigate interference from multipath signals that can affect accuracy, particularly in urban canyons, according to the company.
“We’re at the start of a new era. With the internet of things (IoT) connecting more wireless devices and 5G just around the corner, small cells will have a big role to play,” said Gil Biran, general manager at Oscilloquartz. “This market is set to grow exponentially in the next few years. Small cells will soon be everywhere and that makes precise synchronization essential. Operators urgently need a way to reliably and affordably deliver new levels of phase accuracy.
“We’ve created our OSA 5405 to effectively deliver small cell synchronization in any environment and eliminate all restrictions,” Biran said. “Our new technology radically simplifies GNSS antenna installation. The use of PTP removes the need to compensate for cable delay and extends the reach of GNSS. It enables operators to forget about archaic and expensive RF cables and use simple copper cabling or optical fiber for longer distances. And, with variants that can be positioned in almost any location, it provides strictly accurate timing precisely where it’s needed.”
The compact design and power-over-Ethernet capabilities of the indoor- or outdoor-mounted OSA 5405 enable synchronization at the edge of the mobile network. This creates dramatic reductions in complexity and power requirements as well as lower costs for installation and operation.
Another feature of the new technology is IP connectivity, so that synchronization becomes another element of the internet of things.
The OSA 5405’s highly precise GNSS-sourced synchronization is supported by network-based Sync-E and PTP backups. In high-rise buildings it can also deliver synchronization recovered from the GNSS smart receiver over optical fiber.
The ADVA FSP Network Manager with comprehensive Syncjack assurance guarantees efficient operation.
“Make no mistake; the launch of our OSA 5405 is a major milestone in the progress towards mass-scale small cell deployment,” said Nir Laufer, product line director at Oscilloquartz. “With its plug-and-play simplicity, miniscule form factor and multiple timing functions in a single device, this is a key technology for 5G networks and the IoT.
“Currently deployed in trials with major carriers, it will shortly be available to all operators looking to harness next-generation synchronization precisely where it’s needed,” Laufer said.
The new Scout B-330 UAV in front of the Swiss Alps.
Swiss-based Aeroscout, a long-term partner of lidar manufacturer Riegl, has unveiled the Scout B-330 UAV helicopter.
The Scout B-330 is built with a payload capacity of up to 50 kg. (110 lbs), flight endurance of at least three hours, and the capability of flying at high altitudes (up to 3,000 meters above sea level) in a typical mission scenario. This includes a full autonomous take-off sequence, a mission flight at variable speed and a landing sequence.
The Scout B-330 is specifically designed for lidar-based powerline mapping missions. According to Aeroscout, it sets a benchmark in the long-endurance UAV class with its combination of flexibility, Swiss quality and competitive pricing.
“After one year of intense development, we are very excited to introduce our new Scout UAV system to the public,” said Christoph Eck, Aeroscout founder and CEO. “The reactions here at AUVSI are extremely positive and encouraging, we are very motivated for the serial production out of the system.”
The Scout B-330 pairs with Riegl airborne and unmanned lidar sensors such as the Riegl VP-1 Helicopter Pod, the Riegl VUX-1UAV lightweight UAV laser scanner and the Riegl VUX-1LR lightweight, long-range airborne laser scanner.
The B-330 was introduced at the AUVSI Xponential show held in Dallas in May. Those who missed that show can see it at the Commercial UAV Show in Las Vegas this October.
Rockwell Collins and QinetiQ have signed a global alliance agreement to collaborate on the development of next-generation, multi-constellation open-service and secure GNSS receivers.
The effort will support the mission needs of military, government and critical national infrastructure.
The family of receivers being developed will provide military, government and professional users the flexibility of selecting relevant GNSS capability to meet operational, geographical or budgetary needs and provide GNSS accuracy and timing.
This will improve safety, increase mission effectiveness and reduce operational costs for ground troops, vehicles and high-dynamics GNSS-guided weapons, Rockwell Collins said.
Rockwell Collins is major contractor for secure military GPS receivers and QinetiQ is an expert in the field of open-service solutions with access to critical satellite navigation system technologies that enable the development of multi-constellation solutions.
“This alliance agreement with QinetiQ is a great opportunity to bring together our strengths,” said Colin Mahoney, senior vice president of international and service solutions for Rockwell Collins. “Working together, our customers will experience unprecedented levels of availability, accuracy and assurance of positioning, navigation and timing for conducting their missions.”
“As we move into the era of multi-constellation satellite receivers, this market-leading agreement and the investments of both companies sends a clear message to our customers and shareholders that QinetiQ and Rockwell Collins are taking every step necessary to stay at the forefront of GNSS technical development and product delivery,” said Steve Wadey, CEO of QinetiQ. “The development will be centered in Europe, led from the U.K., supporting the global market.”
OVER THE YEARS, many philosophers tried to describe the phenomenon of inertia but it was Newton, in his Philosophiæ Naturalis Principia Mathematica, who unified the states of rest and movement in his First Law of Motion. One rendering of this law states: Every body continues in its state of rest, or of uniform motion in a straight line, unless it is compelled to change that state by forces impressed upon it. Newton didn’t actually use the word inertia in describing the phenomenon, but that is how we now refer to it.
In his other two laws of motion, Newton describes how a force (including that of gravity) can accelerate a body. And as we all know, acceleration is the rate of change of velocity, and velocity is the rate of change of position. So, if the acceleration vector of a body can be precisely measured, then a double integration of it can provide an estimate of the body’s position. That sounds quite straightforward, but the devil is in the details. Not only do we have to worry about the constants of integration (or the initial conditions of velocity and position), but also the direction of the acceleration vector and its orthogonal components. Nevertheless, the first attempts at mechanizing the equations of motion to produce what we call an inertial measurement unit or IMU were made before and during World War II to guide rockets.
Nowadays, IMUs typically consist of three orthogonal accelerometers and three orthogonal rate-gyroscopes to provide the position and orientation of the body to which it is attached. And ever since the first units were developed, scientists and engineers have worked to miniaturize them. We now have micro-electro-mechanical systems (or MEMS) versions of them so small that they can be housed in small packages with dimensions of a few centimeters or embedded in other devices.
One problem with IMUs, and with the less-costly MEMS IMUs in particular, is that they have biases that grow with time. One way to limit these biases is to periodically use another technique, such as GNSS, to ameliorate their effects. But what if GNSS is unavailable? Well, in this month’s column we take a look at an ingenious technique that makes use of how the human body works to develop an accurate pedestrian navigation system — one whose accuracy has been checked using drone imagery. As they might say in New York, “Hey, I’m walking (with accuracy) here!”
Satellite navigation systems have achieved great success in personal positioning applications.
Nowadays, GNSS is an essential tool for outdoor navigation, but locating a user’s position in degraded and denied indoor environments is still a challenging task. During the past decade, methodologies have been proposed based on inertial sensors for determining a person’s location to solve this problem.
One such solution is a personal pedestrian dead-reckoning (PDR) system, which helps in obtaining a seamless indoor/outdoor position. Built-in sensors measure the acceleration to determine pace count and estimate the pace length to predict position with heading information coming from angular sensors such as magnetometers or gyroscopes. PDR positioning solutions find many applications in security monitoring, personal services, navigation in shopping centers and hospitals and for guiding blind pedestrians.
Several dead-reckoning navigation algorithms for use with inertial measurement units (IMUs) have been proposed. However, these solutions are very sensitive to the alignment of the sensor units, the inherent instrumental errors, and disturbances from the ambient environment — problems that cause accuracy to decrease over time. In such situations, additional sensors are often used together with an IMU, such as ZigBee radio beacons with position estimated from received signal strength.
In this article, we present a PDR indoor positioning system we designed, tested and analyzed. It is based on the pace detection of a foot-mounted IMU, with the use of extended Kalman filter (EKF) algorithms to estimate the errors accumulated by the sensors.
PDR DESIGN AND POSITIONING METHOD
Our plan in designing a pedestrian positioning system was to use a high-rate IMU device strapped onto the pedestrian’s shoe together with an EKF-based framework. The main idea of this project was to use filtering algorithms to estimate the errors (biases) accumulated by the IMU sensors. The EKF is updated with velocity and angular rate measurements by zero-velocity updates (ZUPTs) and zero-angular-rate updates (ZARUs) separately detected when the pedestrian’s foot is on the ground. Then, the sensor biases are compensated with the estimated errors.
Therefore, the frequent use of ZUPT and ZARU measurements consistently bounds many of the errors and, as a result, even relatively low-cost sensors can provide useful navigation performance. The PDR framework, developed in a Matlab environment, consists of five algorithms:
Initial alignment that calculates the initial attitude with the static data of accelerometers and magnetometers during the first few minutes.
IMU mechanization algorithm to compute the navigation parameters (position, velocity and attitude).
Pace detection algorithm to determine when the foot is on the ground; that is, when the velocity and angular rates of the IMU are zero.
ZUPT and ZARU, which feed the EKF with the measured errors when pacing is detected.
EFK estimation of the errors, providing feedback to the IMU mechanization algorithm.
INITIAL ALIGNMENT OF IMU SENSOR
The initial alignment of an IMU sensor is accomplished in two steps: leveling and gyroscope compassing. Leveling refers to getting the roll and pitch using the acceleration, and gyroscope compassing refers to obtaining heading using the angular rate.
However, the bias and noise of gyroscopes are larger than the value of the Earth’s rotation rate for the micro-electro-mechanical system (MEMS) IMU, so the heading has a significant error. In our work, the initial alignment of the MEMS IMU is completed using the static data of accelerometers and magnetometers during the first few minutes, and a method for heading was developed using the magnetometers.
PACE-DETECTION PROCESS
When a person walks, the movement of a foot-mounted IMU can be divided into two phases. The first one is the swing phase, which means the IMU is on the move. The second one is the stance phase, which means the IMU is on the ground. The angular and linear velocity of the foot-mounted IMU must be very close to zero in the stance phase. Therefore, the angular and linear velocity of the IMU can be nulled and provided to the EKF. This is the main idea of the ZUPT and ZARU method.
There are a few algorithms in the literature for step detection based on acceleration and angular rate. In our work, we use a multi-condition algorithm to complete the pace detection by using the outputs of accelerometers and gyroscopes.
As the acceleration of gravity, the magnitude of the acceleration ( |αk| ) for epoch k must be between two thresholds. If
(1)
then, condition 1 is
(2)
with units of meters per second squared. The acceleration variance must also be above a given threshold. With
(3)
where is a mean acceleration value at time k, and s is the size of the averaging window (typically, s = 15 epochs), the variance is computed by:
. (4)
The second condition, based on the standard deviation of the acceleration, is computed by:
. (5)
The magnitude of the angular rate ( ) given by:
(6)
must be below a given threshold:
. (7)
The three logical conditions must be satisfied at the same time, which means logical ANDs are used to combine the conditions:
C = C1 & C2 & C3. (8)
The final logical result is obtained using a median filter with a neighboring window of 11 samples. A logical 1 denotes the stance phase, which means the instrumented-foot is on the ground.
EXPERIMENTAL RESULTS
The presented method for PDR navigation was tested in both indoor and outdoor environments. For the outdoor experiment (the indoor test is not reported here), three separate tests of normal, fast and slow walking speeds with the IMU attached to a person’s foot (see FIGURE 1) were conducted on the roof of the Institute of Space Science and Technology building at Nanchang University (see FIGURE 2). The IMU was configured to output data at a sampling rate of 100 Hz for each test.
For experimental purposes, the user interface was prepared in a Matlab environment. After collection, the data was processed according to our developed indoor pedestrian dead-reckoning system. The processing steps were as follows: Setting the sampling rate to 100 Hz; setting initial alignment time to 120 seconds; downloading the IMU data and importing the collected data at the same time; selecting the error compensation mode (ZARU + ZUPT as the measured value of the EKF); downloading the actual path with a real measured trajectory with which to compare the results (in the indoor-environment case).
For comparison of the IMU results in an outdoor environment, a professional drone was used (see FIGURE 3) to take a vertical image of the test area (see FIGURE 4). Precise raster rectification of the image was carried out using Softline’s C-GEO v.8 geodetic software. This operation is usually done by loading a raster-image file and entering a minimum of two control points (for a Helmert transformation) or a minimum of three control points (for an affine transformation) on the raster image for which object space coordinates are known. These points are entered into a table. After specifying a point number, appropriate coordinates are fetched from the working set. Next, the points in the raster image corresponding to the entered control points are indicated with a mouse.
FIGURE 3. Professional drone. (Photo: DJI)
For our test, we measured four ground points using a GNSS receiver (marked in black in Figure 4), to be easily recognized on the raster image (when zoomed in). A pre-existing base station on the roof was also used. To compute precise static GPS/GLONASS/BeiDou positions of the four ground points, we used post-processing software. During the GNSS measurements, 16 satellites were visible. After post-processing of the GNSS data, the estimated horizontal standard deviation for all points did not exceed 0.01 meters. The results were transformed to the UTM (zone 50) grid system. For raster rectification, we used the four measured terrain points as control points. After the Helmert transformation process, the final coordinate fitting error was close to 0.02 meters.
For comparing the results of the three different walking-speed experiments, IMU stepping points (floor lamps) were chosen as predetermined route points with known UTM coordinates, which were obtained after raster image rectification in the geodetic software (marked in red in Figure 4).
After synchronization of the IMU (with ZUPT and ZARU) and precise image rectification, positions were determined and are plotted in Figure 4. The trajectory reference distance was 15.1 meters.
PDR positioning results of the slow-walking test with ZARU and ZUPT corrections were compared to the rectified raster-image coordinates. The coordinate differences are presented in FIGURE 5 and TABLE 1.
FIGURE 5. Differences in the coordinates between the IMU slow-walking positioning results and the rectified raster-image results. (Chart: Authors)
Table 1. Summary of coordinate differences between the IMU slow-walking positioning results and the rectified raster-image results. (Data: Authors)
The last two parts of the experiment were carried out to test normal and fast walking speeds. The comparisons of the IMU positioning results to the “true” positions extracted from the calibrated raster image are presented in FIGURES 6 and 7 and TABLES 2 and 3.
FIGURE 6. Differences in the coordinates between the IMU normal-walking positioning results and the rectified raster-image results. (Chart: Authors)FIGURE 7. Differences in the coordinates between the IMU fast-walking positioning results and the rectified raster-image results. (Chart: Authors)Table 2. Summary of coordinate differences between the IMU normal-walking positioning results and the rectified raster-image results. (Data: Authors)Table 3. Summary of coordinate differences between the IMU fast-walking positioning results and the rectified raster-image results. (Data: Authors)
From the presented results, we can observe that the processed data of the 100-Hz IMU device provides a decimeter-level of accuracy for all cases. The best results were achieved with a normal walking speed, where the positioning error did not exceed 0.16 meters (standard deviation). It appears that the sampling rate of 100 Hz makes the system more responsive to the authenticity of the gait.
However, we are aware that the test trajectory was short, and that, due to the inherent drift errors of accelerometers and gyroscopes, the velocity and positions obtained by these sensors may be reliable only for a short period of time. To solve this problem, we are considering additional IMU position updating methods, especially for indoor environments.
CONCLUSIONS
We have presented results of our inertial-based pedestrian navigation system (or PDR) using an IMU sensor strapped onto a person’s foot. An EKF was applied and updated with velocity and angular rate measurements from ZUPT and ZARU solutions.
After comparing the ZUPT and ZARU combined final results to the coordinates obtained after raster-image rectification using a four-control-point Helmert transformation, the PDR positioning results showed that the accuracy error of normal walking did not exceed 0.16 meters (at the one-standard-deviation level). In the case of fast and slow walking, the errors did not exceed 0.20 meters and 0.32 meters (both at the one-standard-deviation level), respectively (see Table 4 for combined results).
Table 4. Summary of coordinate differences between the IMU slow-, normal- and fast-walking positioning results and the rectified raster-image results. (Data: Authors)
The three sets of experimental results showed that the proposed ZUPT and ZARU combination is suitable for pace detection; this approach helps to calculate precise position and distance traveled, and estimate accumulated sensor error.
It is evident that the inherent drift errors of accelerometers and gyroscopes, and the velocity and position obtained by these sensors, may only be reliable for a short period of time. To solve this problem, we are considering additional IMU position-updating methods, especially in indoor environments. Our work is now focused on obtaining absolute positioning updates with other methods, such as ZigBee, radio-frequency identification, Wi-Fi and image-based systems.
ACKNOWLEDGMENTS
The work reported in this article was supported by the National Key Technologies R&D Program and the National Natural Science Foundation of China. Thanks to NovAtel for providing the latest test version of its post-processing software for the purposes of this experiment. Special thanks also to students from the Navigation Group of the Institute of Space Science and Technology at Nanchang University and to Yuhao Wang for his support of drone surveying.
MANUFACTURERS
The high-rate IMU used in our work was an Xsense MTi miniature MEMS-based Attitude Heading Reference System. We also used NovAtel’s Waypoint GrafNav v. 8.60 post-processing software and a DJI Phantom 3 drone.
MARCIN URADZIŃSKI received his Ph.D. from the Faculty of Geodesy, Geospatial and Civil Engineering of the University of Warmia and Mazury (UWM), Olsztyn, Poland, with emphasis on satellite positioning and navigation. He is an assistant professor at UWM and presently is a visiting professor at Nanchang University, China. His interests include satellite positioning, multi-sensor integrated navigation and indoor radio navigation systems.
HANG GUO received his Ph.D. in geomatics and geodesy from Wuhan University, China, with emphasis on navigation. He is a professor of the Academy of Space Technology at Nanchang University. His interests include indoor positioning, multi-sensor integrated navigation systems and GNSS meteorology. As the corresponding author for this article, he may be reached at [email protected].
CLIFFORD MUGNIER received his B.A. in geography and mathematics from Northwestern State University, Natchitoches, Louisiana, in 1967. He is a fellow of the American Society for Photogrammetry and Remote Sensing and is past national director of the Photogrammetric Applications Division. He is the chief of geodesy in the Department of Civil and Environmental Engineering at Louisiana State University, Baton Rouge. His research is primarily on the geodesy of subsidence in Louisiana and the grids and datums of the world.
FURTHER READING
• Authors’ Work on Indoor Pedestrian Navigation
“Indoor Positioning Based on Foot-mounted IMU” by H. Guo, M. Uradziński, H. Yin and M. Yu in Bulletin of the Polish Academy of Sciences: Technical Sciences, Vol. 63, No. 3, Sept. 2015, pp. 629–634, doi: 10.1515/bpasts-2015-0074.
“Usefulness of Nonlinear Interpolation and Particle Filter in Zigbee Indoor Positioning” by X. Zhang, H. Guo, H. Wu and M. Uradziński in Geodesy and Cartography, Vol. 63, No. 2, 2014, pp. 219–233, doi: 10.2478/geocart-2014-0016.
• IMU Pedestrian Navigation
“Pedestrian Tracking Using Inertial Sensors” by R. Feliz Alonso, E. Zalama Casanova and J.G. Gómez Garcia-Bermejo in Journal of Physical Agents, Vol. 3, No. 1, Jan. 2009, pp. 35–43, doi: 10.14198/JoPha.2009.3.1.05.
“Pedestrian Tracking with Shoe-Mounted Inertial Sensors” by E. Foxlin in IEEE Computer Graphics and Applications, Vol. 25, No. 6, Nov./Dec. 2005, pp. 38–46, doi: 10.1109/MCG.2005.140.
• Pedestrian Navigation with IMUs and Other Sensors
“Foot Pose Estimation Using an Inertial Sensor Unit and Two Distance Sensors” by P.D. Duong, and Y.S. Suh in Sensors, Vol. 15, No. 7, 2015, pp. 15888–15902, doi: 10.3390/s150715888.
“Enhancing Indoor Inertial Pedestrian Navigation Using a Shoe-Worn Marker” by M. Placer and S. Kovačič in Sensors, Vol. 13, No. 8, 2013, pp. 9836–9859, doi: 10.3390/s130809836.
“Use of High Sensitivity GNSS Receiver Doppler Measurements for Indoor Pedestrian Dead Reckoning” by Z. He, V. Renaudin, M.G. Petovello and G. Lachapelle in Sensors, Vol. 13, No. 4, 2013, pp. 4303–4326, doi: 10.3390/s130404303.
“Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements” by A. Ramón Jiménez Ruiz, F. Seco Granja, J. Carlos Prieto Honorato and J. I. Guevara Rosas in IEEE Transactions on Instrumentation and Measurement, Vol. 61, No. 1, Jan. 2012, pp. 178–189, doi: 10.1109/TIM.2011.2159317.
• Pedestrian Navigation with Kalman Filter Framework
“Indoor Pedestrian Navigation Using an INS/EKF Framework for Yaw Drift Reduction and a Foot-mounted IMU” by A.R. Jiménez, F. Seco, J.C. Prieto and J. Guevara in Proceedings of WPNC’10, the 7th Workshop on Positioning, Navigation and Communication held in Dresden, Germany, March 11–12, 2010, doi: 10.1109/WPNC.2010.5649300.
“A Robust Method to Detect Zero Velocity for Improved 3D Personal Navigation Using Inertial Sensors” by Z. Xu, J. Wei, B. Zhang and W. Yang in Sensors Vol. 15, No. 4, 2015, pp. 7708–7727, doi: 10.3390/s150407708.
“Since then, we’ve made a lot of progress,” Steve Malkos, Google technical program manager, told GPS World.
Frank van Diggelen and Mohammed Khider joined Malkos in hosting a half-day tutorial at ION GNSS+ 2016 in September that detailed how to access and use GPS measurements from Android devices.
“We (Google) launched a new website around our efforts with GNSS Measurements that has the latest updates about all things GNSS, such as supported devices, collection tools and analysis tools,” Malkos said.
The Android GNSS Analysis Tool shows how users can select and run the analysis on a per-satellite basis. This tool now supports multi-constellation and dual frequency (L1 and L5) by default. (Credit: Google)
Also, many devices releasing this year will support multi-constellation raw GNSS measurements for the first time. The Phone section on Google’s website shows the latest phones that support multi-constellation measurements. “Google also has launched a device with this capability, one of the first in the world,” Malkos said.
Android O, the next version of Android, will include new GNSS measurement features, such as true multi-constellation support with GNSS measurements (API supported constellations include GPS, SBAS, GLONASS, QZSS, BeiDou and Galileo), measurement support on multiple frequencies (including L1 and L5) and reported AGC (accumulated gain control) jamming detector.
This plot shows the generated output from the Android GNSS Analysis Tool: Signals strengths for the top four satellites per constellation, Skyplots, C/N0 plots, clock continuity or discontinuity, WLS output, PRR and PRR residuals. (Credit: Google)
Google hosts ION GNSS tutorial
Google is hosting a full-day tutorial, “Raw GNSS Measurements from Android Phones,” at ION GNSS+ 2017, which will be held Sept. 25–29 in Portland, Oregon.
The interactive course covers:
The Android Software Stack. Learn how GNSS measurement data flows through the Android software stack. Google will also show attendees where to find the definitions of the different data structures and identify which ones are available at the Application layer.
Updates to Android O. Preview the new GPS-related changes that are slated for Android O.
Description of the available data. Review the data that is accessible in Android, the definitions of the different types of GNSS measurements, their physical meaning and how to use them for analysis and location.
Using the data. Collect GNSS measurements outside and download the data from a provided test device to do some processing. Google will provide software tools that allow participants to log data from an Android Nougat or Android O device, view the raw measurements, and complete basic measurement analysis and position computation.
Examples. Finally, Google will give those who attend specific examples of research projects and applications that users can develop with the tools and knowledge obtained in the class, such as how to build a GNSS data analysis app or how to build a crowd-sourced jammer detector.
To help the Android Measurements team tailor this tutorial to your needs, fill out this form with additional items you’d like covered in the class.
Analog Devices and Renesas Electronics Corporation are collaborating on a system-level 77/79-GHz radar sensor demonstrator to improve advanced driver assistance systems (ADAS) applications and enable autonomous driving vehicles.
The new demonstrator combines the RH850/V1R-M micro-controller from the Renesas autonomy Platform and ADI’s Drive360 28nm CMOS RF-to-bits technology.
The system-level operation of these two technologies will enable earlier detection of smaller and faster moving objects at greater distances, according to the companies. It will also lower radar system integration efforts and reduce evaluation risks, development cost and time to market for automotive OEMs and Tier One suppliers, the companies said.
Analog Devices Drive360 28nm CMOS RADAR technology platform builds on the company’s established ADAS, MEMS, and radar portfolio to enhance sensor performance for ADAS applications with the world’s first automotive radar technology based on advanced 28nm CMOS with RF performance for target identification and classification. High output power enables greater range and identification of smaller objects, while lowest phase noise enables best unambiguous detection of smaller objects in the presence of large objects.
Renesas offer automotive end-to-end solutions from secure cloud connectivity and sensing to autonomous control. Renesas autonomy Platform is an open platform for ADAS and automated driving, supported by Renesas’ sustainable and scalable SoC and MCU roadmaps. The RH850/V1R-M MCU was specifically designed for use in radr applications.
The Analog Devices Drive360 28nm CMOS RADAR technology platform builds on the company’s established ADAS, MEMS and radar technology portfolio widely used throughout the automotive industry for the past 20 years.
ADI’s high-performance radar solution enables earlier detection of smaller and faster moving objects. High-output power enables greater range and identification of smaller objects, while low phase noise enables unambiguous detection of smaller objects in the presence of large objects. See Analog Devices’ Drive360 video here.
The new Renesas autonomy platform is an open, innovative and trusted platform for ADAS and automated driving, supported by Renesas’ sustainable and scalable SoC and MCU roadmaps, the company said.
The RH850/V1R-M MCU was specifically designed for use in RADAR applications as part of the sustainable and scalable portfolio. The new MCU includes optimized programmable digital signal processing, dual CPU cores each operating at 320 megahertz with high-speed flash of 2 MB and 2 MB internal RAM, while meeting industry temperature requirements.
Laser Technology’s MapSmart app for Android is a tool for expert field data collection without complicated equipment, the company said.
The software is designed for quick and accurate mapping of anything, including stockpile volumes, with or without GPS coordinates for every data point.
The survey-quality mapping app, using the smart device’s internal GPS or the user’s own external GPS, integrates with LTI TruPulse lasers and enables users to establish an origin and begin capturing field data in minutes.
MapSmart:
offers four mapping methods to accommodate user preferences
provides an intuitive interface with icons and buttons
organizes and classifies data to ease the process of decrypting field measurements in the office
enables real-time addition of height and missing line values to mapped features
delivers advanced image capabilities, including tablet photo association with data points and TruPoint 300 image integration
supports a variety of report formats and wireless data transfer.
The smart features and remote-fire capabilities are especially useful for stockpiles, where users can measure and calculate the volume and tonnage of any material from a safe location.
In this article, author Urs Hugentobler looks at the history of laser ranging to navigation satellites, how that ranging has improved the accuracy of the orbits of those satellites and what the future portends for this important contribution to space geodesy.
INNOVATION INSIGHTS with Richard Langley
THE LASER. It might not be in the top 10 of the most important inventions of all time, but Time magazine rated it among the most important developments of the 20th century, listing it fifth after the automobile, the radio, the television and the transistor. Lasers are now ubiquitous: they scan our purchases at the supermarket checkout; they let us read and write data on compact discs; they have replaced the scalpel in many operating theaters; and they play major roles on the battlefield with laser-guided munitions. However, one of the first practical uses of the laser was in precisely determining the orbits of satellites.
Initial experiments in ranging to satellites carrying corner-cube retroreflectors began in 1964 just a few years after the laser was invented in 1960. Satellite laser ranging (SLR) stations were built in several countries, and a number of multi-instrument satellites with retroreflectors were launched by the U.S. and other nations along with dedicated spherical satellites with no electronic instrumentation — just the retroreflectors covering the satellite’s surface. The first of these was the Laser Geodynamics Satellite, or LAGEOS. It was designed by NASA and launched in 1976. LAGEOS and the other satellites carrying retroreflectors played a significant part in NASA’s Crustal Dynamics Project (CDP). Initiated in 1979, the CDP promoted the use of SLR and very long baseline interferometry to improve our understanding of plate tectonics, the rotational dynamics of the Earth, and the structure of the Earth’s gravity field.
As a post-doctoral fellow at the Massachusetts Institute of Technology and later at the University of New Brunswick, I participated in the CDP with analyses of lunar laser ranging (LLR) data. Ranging to reflectors placed on the moon’s surface by Apollo astronauts as well as those on the Russian Lunokhod rovers was a bit more difficult than ranging to satellites given the larger distances to the reflectors and the much weaker return pulses. Among other advances, LLR was the first technique to confirm the existence of variations in the spin of the Earth with a periodicity of around 50 days.
But let’s get back to SLR. Today, thanks in large measure to the International Laser Ranging Service, ranging data is routinely collected on more than 70 satellites and lunar reflectors. Included is a growing list of GNSS satellites equipped with corner-cube retroreflectors. Laser ranging to GNSS satellites is instrumental is better modeling the orbits of these satellites. Among other benefits, better GNSS satellite orbits result in better receiver position accuracies — accuracies needed to improve monitoring of crustal strain, for example, including that associated with earthquakes.
In this month’s column, we take a look at the past, present and future of laser ranging to GNSS satellites and how laser ranging and microwave ranging are mutually beneficial. They are truly kindred spirits.
Nighttime ranging at NASA’s Next Generation SLR system at Goddard Space Flight Center, Maryland. (Credit: Felipe Hall/HTSI)
Satellite laser ranging or SLR has been an indispensable independent tool for validating the precise orbits determined for GNSS satellites using microwave pseudorange and carrier-phase observations for several decades. SLR has allowed researchers to identify several orbit-modeling issues. Adding albedo radiation pressure and antenna thrust, among other effects, into the GPS orbit model allowed them to eliminate the observed bias between microwave- and SLR-derived orbits. For the first Galileo satellites launched, SLR residuals indicated severe orbit modeling issues caused by the different shape of Galileo satellite bodies compared to those of GPS. In the future, all GNSS satellites will be equipped with laser retroreflectors, a big challenge for researchers concerning tracking scenarios and observation planning to make economic use of the ground equipment.
In this article, we will take a brief look at the history of laser ranging to navigation satellites, how that ranging has improved the accuracy of the orbits of those satellites, and what the future portends for this important contribution to space geodesy.
VALIDATION OF GNSS ORBITS
FIGURE 1. Operating principle of satellite laser ranging.
In 1964, only four years after Theodore Maiman built the first laser, the first laser echoes were obtained from NASA’s Explorer 22 satellite. SLR rapidly developed into an indispensable tool for precise orbit determination, gravity field determination, and Earth system research.
FIGURE 1 shows the principles of SLR operation. Essentially, an SLR station fires a series of laser pulses at passing satellites equipped with corner-cube retroreflectors, and the relatively few photons returned are collected by a telescope. The station electronics measures the round-trip travel times of the laser pulses. From these measurements, the coordinates of the SLR station or the satellite’s orbit can be determined.
Observations by a global network of SLR stations are coordinated by the International Laser Ranging Service (ILRS), which, like the International GNSS Service, is one of the space geodetic services of the International Association of Geodesy (IAG).
FIGURE 2. Retroreflector array on GPS Block IIA satellites SVNs 35 and 36.
Since the early 1990s, the ILRS has tracked GNSS satellites supporting the independent validation of the microwave-derived precise orbits. Two Block IIA GPS satellites, SVN35 and SVN36, were equipped with retroreflectors (see FIGURE 2) and they were routinely tracked from their launches in 1993 and 1994, respectively, until their decommissioning in 2013 and 2014 (actually, SVN36 was subsequently briefly reactivated in 2015 so data is available for that satellite until that year). Also in the 1990s, the ILRS started to track GLONASS satellites in support of the International GLONASS Experiment (IGEX-98). There is a retroreflector array on all GLONASS satellites (see FIGURE 3).
Range residuals of GPS and GLONASS satellites were studied in the early years by a number of different research groups. Most of their analyses showed a bias of about –5.5 centimeters for GPS satellite orbits derived from microwave tracking data by the IGS while the accuracy of the latter was estimated to about 5 centimeters. For GLONASS orbits, a negative bias of about –4 centimeters was identified, too. The accuracy of the orbits was, however, at the 10–15 centimeter level. These validation results supported several model improvements for GPS satellite orbits including, in particular, the handling of solar and Earth albedo radiation pressure and antenna thrust, reducing the observed SLR bias with respect to the IGS orbits to 1.3 centimeters with a standard deviation of about 2 centimeters.
“What are radiation pressure and antenna thrust?” you might ask. The photons making up the light coming directly from the sun or reflected from the Earth’s surface (albedo) impinge on a satellite and transfer some of their energy to it. Solar radiation pressure – the force due to the impact of the photons – is tiny, but its continuing presence has a strong perturbing effect on satellite orbits. Antenna thrust is also a small force. The transmission of GPS navigation signals results in a continuously acting reactive force in the radial direction acting on the satellite.
FIGURE 4. Retroreflector array on Galileo satellites (at bottom of satellite, below antenna array).
SLR also plays an essential role for calibrating improved radiation pressure models for the new satellite systems. All Galileo satellites have retroreflectors (see FIGURE 4), and the orbits of the first satellites to be launched, generated using the classical extended radiation pressure model of the Center for Orbit Determination in Europe (operating in the framework of the IGS Multi-GNSS Pilot Project or MGEX), had SLR residuals as large as 20 centimeters for passes with a small beta angle. (The beta angle is the angle between the sun and a satellite’s orbital plane.) The origin of this behavior is the elongated shape of the Galileo satellites compared to the more-or-less cubic shape of GPS satellites, causing much larger variations of the satellite cross-section exposed to the sun while orbiting the Earth. The observed SLR residuals triggered the development of improved radiation pressure models for Galileo satellites.
All BeiDou satellites are also believed to be equipped with retroreflectors (see FIGURE 5). As the estimated longitude of geostationary GNSS satellites such as those in the BeiDou constellation is highly susceptible to biases due to the small motion of the satellites with respect to the tracking stations, SLR may play an important role for precise orbit determination of this category of satellite.
FIGURE 5. Retroreflector array on BeiDou satellites.
The satellites of the Indian Regional Navigation Satellite System (IRNSS), also known as the Navigation with Indian Constellation system or NavIC, also carry retroreflectors (see FIGURE 6) and have been tracked by SLR stations. However, little publicly available microwave tracking data yet exists. Therefore, up to now, precise orbit determination heavily relies on SLR observations.
FIGURE 6. Retroreflector array on NavIC satellites.
MORE APPLICATIONS OF SLR FOR GNSS
Because GNSS is a one-way measurement technique, only pseudoranges and carrier phases can be measured, and clock synchronization is indispensable for positioning and orbit determination. Radial orbit errors can therefore be absorbed to a large degree by satellite clock corrections. For the very stable clocks on board Galileo satellites, the SLR residuals show the same behavior as the microwave-derived clock corrections indicating that the clock corrections are, in fact, caused by radial orbit errors. SLR therefore provides a way to break this correlation and to separate radial orbit errors and satellite clock corrections. This makes it possible to study and to characterize the physical behavior of onboard clocks including temperature-induced clock variations.
Separation of orbit errors and satellite clock variations is crucial when using the first two Full Operational Capability Galileo satellites, which were released into wrong orbits, for relativistic experiments. In a dual launch on Aug. 22, 2014, the two satellites were put into orbits with an initial eccentricity of 0.233 and orbit height of 19,800 kilometers due to a malfunction of the launcher third stage. With a sequence of maneuvers, the satellite orbit heights could be increased to 22,600 kilometers (compared to the planned height of 23,200 kilometers) and the eccentricity was decreased to 0.156. The satellites are, nevertheless, fully functional, and the very stable hydrogen masers on board should allow scientists to improve the uncertainty of the relativistic redshift parameter α beyond the current value determined in 1976 using the Gravity Probe A satellite. Regular SLR tracking of the two satellites plays an essential role in this experiment to separate clock variations due to orbit errors from those caused by the gravitational redshift.
Eventually, SLR may also be used as a tool for high-precision time synchronization of stable GNSS clocks combining one-way laser transmissions with two-way active laser operation, similar to the concept of the European Laser Timing experiment foreseen using the Atomic Clock Ensemble in Space (ACES) on the International Space Station and already tested for BeiDou satellites.
SLR TRACKING OF THE GNSS CONSTELLATIONS
In the near future, more than 100 GNSS satellites carrying retroreflectors will be operational. This includes GPS Block III satellites, which will carry retroreflectors starting with SV-9. Tracking the full GNSS constellation will pose a big challenge for the ILRS concerning economic use of its ground equipment. Optimized tracking scenarios and session planning strategies will be indispensable.
Already today, the ILRS regularly tracks a large number of GNSS satellites. TABLE 1 shows the number of SLR normal points from ranging to the various GNSS constellations available at the ILRS data centers since 2010. Normal points are compressed full-rate data obtained by averaging individual range measurements typically over five-minute intervals. As part of the Laser Ranging to GNSS Spacecraft Experiment or LARGE project of the ILRS, the tracking of GLONASS satellites was extended to the entire satellite constellation as shown in FIGURE 7.
FIGURE 7. Number of SLR normal points per month for GLONASS satellites.
To assess the capability of SLR for GNSS precise orbit determination based on the number of tracking stations and the distribution of observations, we performed a simple simulation. The covariance analysis included observations of a single SLR station compared to networks of 6 and 17 globally distributed stations. For each station, three normal points were simulated per satellite pass for a full 24-satellite Galileo constellation: two observed at 30° rising and setting elevation angles and one at maximum elevation angle. No unfavorable weather conditions were considered and observations of different stations were assumed to be uncoordinated.
Formal errors of the determined orbits are shown in FIGURE 8 for the radial, along-track, and cross-track components. As expected, orbits determined with observations from one day’s observations by a single station reach formal errors in the few 10s of kilometers range (plot on the left in the first row). If observations from three days are used for orbit determination, the errors on the middle day reduce to about 100 meters (right, first row). The situation significantly improves if a global network of six stations is considered. Even for a single day of observations, an orbit precision of a few decimeters is reached (left, second row) while the orbit uncertainty further decreases to a few centimeters if observations from three days are used (right, second row). If, however, in an effort to reduce the number of observations per pass, only measurements at satellite culmination are acquired, the orbit precision is in the kilometer range for a six-station network and observations from one day (left, third row). If observations from three days are used, the orbit precision is at the meter level (right, third row). Using three normal points per pass for a 17-station network, the orbit precision reaches a few centimeters even within one day (left, last row) and about 1 centimeter for observations from three days (right, last row). It should be noted that the covariance analysis does not consider any systematic observation or orbit modeling error.
FIGURE 8. Formal errors of Galileo orbits in radial (red), along-track (green) and cross-track (blue) directions. First row: one SLR station, 1-day arc (left), middle of 3-day arc (right); second row: six stations, 1-day arc (left), 3-day arc (right); third row: six stations with tracking only at culmination, 1-day arc (left), 3-day arc (right); fourth row: 17 stations, 1-day arc (left), 3-day arc (right). Note the different scaling for the various plots.
This simulation is very simple and not very realistic, but nevertheless indicates the capability of precise orbit determination for GNSS satellites using a limited number of observations per station. The simulations demonstrate two facts. Firstly, even with just two or three normal points per satellite of a GNSS constellation, a significant fraction of the observation time of a station is required. Typically, a mid-latitude station can acquire about 60 normal points per day for a 24-satellite constellation, amounting to several hours of observation time per day. Secondly, the improvement in formal orbit accuracy only increases with the square root of the number of stations. More important than the number of normal points is their distribution along the orbit requiring SLR observations from several stations distributed over the globe.
These two findings make it obvious that coordination among SLR stations is indispensable for making economic use of the observing time of SLR stations while providing good coverage of normal points along all satellite orbits. To cope with weather conditions, this coordinated scheduling of GNSS SLR tracking may have to be optimized in real time.
CONCLUSIONS
SLR has played an important role in validating GNSS-derived satellite orbits for the past several decades. For new GNSS constellations and new orbit types, SLR proves to be essential for calibrating radiation pressure models and allows us to separate orbit- and temperature-induced variations of onboard clocks. Eventually, the role of SLR will become even more important by contributing to the precise orbit determination of GNSS satellites. Given the large number of GNSS satellites from several constellations equipped with retroreflectors, coordination of observation scheduling among SLR stations will be crucial for optimizing the benefit-to-cost ratio.
Concerning the distribution of SLR observations over the constellations, the following conclusions may be drawn:
For the validation and calibration of radiation pressure models, it is sufficient to acquire well-distributed observations along the orbit of one satellite for each constellation block type for a range of solar beta angles, that is, of one satellite block type per orbital plane.
For contributing to precise orbit products, optimally combined with microwave GNSS observations, the tracking of all satellites of a constellation is needed. This requires a coordinated scheduling of observations among SLR stations.
For determination of the gravitational redshift parameter using the two Galileo satellites in eccentric orbits, good coverage of the orbits of both satellites is required (as long as the satellites run on one of the onboard hydrogen maser clocks).
For BeiDou and NavIC geostationary satellites, SLR coverage is needed for all satellites to resolve biases in the microwave tracking technique.
In the long term, SLR observations could contribute, together with microwave observations, in providing operational high-precision orbit products for all GNSS constellations jointly by the ILRS and the IGS in the framework of the IAG’s Global Geodetic Observing System.
ACKNOWLEDGMENTS
This article is based on the invited paper “Ranging the GNSS Constellation” presented at the 20th International Workshop on Laser Ranging held in Potsdam, Germany, Oct. 10–14, 2016. Figure 1 was adapted from an image in “Expert Advice: Laser Reflectors to Ride on Board GPS III” published by GPS World. GPS, Galileo, BeiDou and NavIC retroreflector images obtained from the ILRS. The GLONASS retroreflector image was obtained from ISS Reshetnev. Opening photo: Nighttime ranging at NASA’s Next Generation SLR system at Goddard Space Flight Center, Maryland (Credit: Felipe Hall/HTSI).
URS HUGENTOBLER is a professor of satellite geodesy at the Technische Universität München, Germany, and head of the Satellite Geodesy Research Facility in the Institute for Astronomical and Physical Geodesy. He is also a former chair of the IGS Governing Body. His research activities include precise positioning using GNSS, precise orbit determination and modeling, reference-frame realization, clock modeling and time transfer, using both the legacy and new satellite systems. Hugentobler obtained his Ph.D. from the University of Bern, Switzerland, in 1997.
FURTHER READING
Author’s Conference Paper
“Ranging the GNSS Constellation” by U. Hugentobler, presented at the 20th International Workshop on Laser Ranging held in Potsdam, Germany, Oct. 10–14, 2016.
Early Work on Satellite Laser Ranging
“Satellite Laser Ranging: Current Status and Future Prospects” by J.J. Degnan in IEEE Transactions on Geoscience and Remote Sensing, Vol. GE-23, No. 4, July 1985, pp. 398–413, doi: 10.1109/TGRS.1985.289430.
“Reflection of Ruby Laser Radiation from Explorer XXII” by H.H. Plotkin, T.S. Johnson, P. Spandin and J. Moye in Proceedings of the IEEE, Vol. 53, No. 3, March 1965, pp. 301–302, doi: 10.1109/PROC.1965.3694.
Early Work on GPS Orbit Modeling
“Extended Orbit Modeling Techniques at the CODE Processing Center of the International GPS Service for Geodynamics (IGS): Theory and Initial Results” by G. Beutler, E. Brockmann, W. Gurtner, U. Hugentobler, L. Mervart, M. Rothacher and A. Verdun in Manuscripta Geodaetica, Vol. 19, 1994, pp. 367–386.
The International Laser Ranging Service
“The International Laser Ranging Service” by M.R. Pearlman, J.J. Degnan and J.M. Bosworth in Advances in Space Research, Vol. 30, No. 2, July 2002, pp. 135–143, doi: 10.1016/S0273-1177(02)00277-6.
SLR Tracking of GNSS Constellations
“Satellite Laser Ranging to GPS and GLONASS” by K. Sósnica, D. Thaller, R. Dach, P. Steigenberger, G. Beutler and D. Arnold in Journal of Geodesy, Vol. 89, No. 7, July 2015, pp. 725–743, doi: 10.1007/s00190-015-0810-8.
“IRNSS Orbit Determination and Broadcast Ephemeris Assessment” by O. Montenbruck, P. Steigenberger and S. Riley in Proceedings of ION ITM 2015, the 2015 International Technical Meeting of The Institute of Navigation, Dana Point, California, Jan. 26–28, 2015, pp. 185–193.
“Initial Results of Precise Orbit and Clock Determination for COMPASS Navigation Satellite System” by Q. Zhao, J. Guo, M. Li, L. Qu, Z. Hu, C. Shi and J. Liu in Journal of Geodesy, Vol. 87, No. 5. May 2013, pp. 475–486, doi: 10.1007/s00190-013-0622-7.
“Contribution of SLR Tracking Data to GNSS Orbit Determination” by C. Urschl, G. Beutler, W. Gurtner, U. Hugentobler and S. Schaer in Advances in Space Research, Vol. 39, No. 10, 2007, pp. 1515–1523, doi: 10.1016/j.asr.2007.01.038.
Effect of Radiation Pressure on GNSS Satellite Orbits
“CODE’s New Solar Radiation Pressure Model for GNSS Orbit Determination” by D. Arnold, M. Meindl, G. Beutler, R. Dach, S. Schaer, S. Lutz, L. Prange, K. Sósnica, L. Mervart and A. Jäggi in Journal of Geodesy, Vol. 89, No. 8, Aug. 2015, pp. 775–791, doi: 10.1007/s00190-015-0814-4.
“Enhanced Solar Radiation Pressure Modeling for Galileo Satellites” by O. Montenbruck, P. Steigenberger and U. Hugentobler in Journal of Geodesy, Vol. 89, No. 3, March 2015, pp. 283–297, doi: 10.1007/s00190-014-0774-0.
“Impact of Earth Radiation Pressure on GPS Position Estimates” by C.J. Rodriguez-Solano, U. Hugentobler, P. Steigenberger and S. Lutz in Journal of Geodesy, Vol. 86, No. 5, May 2012, pp. 309–317, doi: 10.1007/s00190-011-0517-4.
“Test of the Gravitational Redshift with Stable Clocks in Eccentric Orbits: Application to Galileo Satellites 5 and 6” by P. Delva, A. Hees, S. Bertone, E. Richard and P. Wolf in Classical and Quantum Gravity, Vol. 32, No. 23, 2015, doi: 10.1088/0264-9381/32/23/232003.
Road Navigation Using Multiple Dissimilar Environmental Features
New navigation paradigms combining GNSS and inertial with additional sensors can increase overall reliability and power robust road navigation. A feasibility study tests a barometer, a magnetometer and a camera looking at road signs, and concludes that such sensors examining environmental features can supply the necessary context for frequently traveled or shared routes.
By Debbie Walter, Paul D. Groves, Bob Mason, Joe Harrison, Joe Woodward and Paul Wright
Where a robust and reliable position solution is required, it is necessary to combine GNSS with other technologies. Dead-reckoning is only suitable for bridging short outages. For robustness against longer GNSS outages, alternative position fixing techniques are needed. Radio-based signals have been excluded from this study as they are either not yet mature or are, like GNSS, susceptible to jamming, though they may still play a part in the final navigation solution.
For land navigation in particular, a new approach is therefore needed. Environmental features provide a potential source of location information. These include buildings or parts thereof, signs, roads, rivers, terrain height, sounds, smells and even variations in the magnetic and gravitational fields. Visual navigation technologies are being developed and are likely to be complementary to the feature-matching discussed in this article; however, they will not be directly discussed. The environmental features will be integrated with dead reckoning to provide robust positioning.
The overall solution is to place hardware within a batch of vehicles, comprising multiple sensors, including a GNSS receiver and sensors for dead reckoning. Road map matching could also be included. During normal usage, the GNSS receiver is used for positioning and a database is updated with the feature information from all the sensors accompanied by location stamps from the GNSS-based position solution.
As the multiple vehicles travel around an area, the database is built up for these routes. In the event that the GNSS receiver does not receive sufficient signals to maintain an accurate position, the database is called upon for navigation by environmental feature matching. In this scenario, the sensors continue to take measurements and, by combining the knowledge of the last known location, dead reckoning and the sensor’s outputs, the positioning algorithm draws upon the database to estimate a positioning solution. This method is shown in Figure 1 and Figure 2.
Figure 1. Basic workflow mode for collecting data. Source: GPS World
Figure 2. Basic workflow mode for navigation using collected data.
This navigation system relies upon the roads being travelled on a regular basis so that the “maps” created from the sensor’s outputs are kept up to date and therefore valid. The most likely users of this technology would be fleets of vehicles that can share the mapping information. To focus on a typical system, use in emergency vehicles was considered. Knowing your position is vital in an emergency vehicle, and a system that incorporates a back-up to GNSS would be advantageous. The motivation for maintaining a continuous positioning solution is that, when moving within a complex environment, it is necessary to maintain the integrity of the current position. In emergency situations, delays are not acceptable and integrity is vital. There will be no point in time when the vehicle can be delayed to obtain a position fix.
Although this system will be designed with emergency service vehicles such as ambulances and police cars in mind, it could also be used in wider applications such as fleet management and tracking devices. Ultimately, crowd sourcing or cooperative techniques could be used to pool information from different vehicles equipped with the system. With a very large number of vehicles maintaining the feature database, the system could adapt to changes in the environment very quickly.
To reliably achieve meters-level positioning across a range of different challenging environments, a paradigm shift is needed. We need to use as much information as we can cost-effectively obtain from many different sources in order to determine the best possible navigation solution in terms of both accuracy and reliability.
This new approach to navigation and real-time positioning in challenging environments requires many new lines of research to be pursued.
ROAD EXPERIMENT
A set of sensors with a GNSS receiver were attached to a car and driven in closed loops around Stoke-on-Trent on multiple road types over multiple days. The loops were repeated three times on each day on four road types and then repeated over three consecutive days. The sensors used can be seen in Table 1.
Table 1. Sensors used in the road experiments.
The accelerometer, air quality sensor, barometer, dust sensor, light sensors and microphone interfaced with an Arduino microprocessor which outputted the signals from the sensors to a laptop. The Arduino sensors had a data rate of 20 measurements per second. There was an individual accelerometer (attached to the axel of the vehicle) for use in identifying road texture. There are also accelerometers that form part of the inertial measurement unit (IMU), and these were used for dead reckoning.
The onset of movement as recorded from the IMU was used to assist in identifying the beginning of each circuit. During the car journeys, there were two experimenters, one to drive the car and another to monitor the sensors. There were 5–10 minutes between each round; during this time, the sensors would be turned off and then restarted. The equipment was designed for the outputs of the sensors to be post-processed.
The four classes of road were suburban, urban, rural and high-speed road. The route taken and a view from Google Street View showing the general type of landscape traveled through is shown in Figure 3.
Figure 3. The urban road type used in the road experiment.
Figure 3. The suburban road type used in the road experiment.
Figure 3. The rural road type used in the road experiment.
Figure 3. The high-speed road type used in the road experiment.
A road experiment travelled the routes, using GPS receivers with the Arduino, video camera and the IMU so that GPS time could be used as a constant for the various sensors.
WHOLE ROUTE ANALYSIS
The outputs from the sensors were evaluated initially for their cross correlation over the whole route. This process assessed whether the data from different runs over the same terrain were similar and thus had a high cross correlation. This is vital for this map-building method of navigation. This section deals only with sensors that produce continuous output. The next section discusses discrete features.
Cross-Correlation Coefficients. The correlation coefficient (see online version of this article for derivation equations) is used to calculate the cross-correlation of two rounds of sensor data. The cross correalation coefficient is a normalized value. If a signal is correlated with itself, at zero offset (autocorrelation) this would give a value of 1; entirely uncorrelated data gives 0. Signals 180° out of phase would give a correlation value of –1.
The cross-correlation coefficients are shown in Table 2 for all of the sensors. It shows the coefficients for the four different road types using combinations of rounds (round 1 and 2, round 2 and 3 and round 1 and 3 for each three days) from the same days and the average of the coefficients for all the combinations. The sensors with higher coefficients are discussed in more detail in the following subsections. Road signs do not have cross-correlation coefficients; they are treated differently as this is a discrete measurement.
Accelerometer. The magnitude of the acceleration from a accelerometer triad was used in this experiment as a method of measuring road texture. A zoomed-in section of the acceleration as recorded from the accelerometer against the distance traveled can be seen in Figure 4.
Figure 4. Profile from accelerometer attached to axle.
It is difficult to see similarities in the output from the different rounds, although the accelerometer can show movement from stationary to driving and this was used to initialize the sensor outputs from the XSens IMU. This is shown in Figure 5 at 44s.
Figure 5. Accelerometer data showing vehicle setting off.
Barometer. The barometer measures height change of the vehicle. This sensor consistently produced the highest cross-correlation coefficient, shown in Figure 6.
Figure 6. Comparison of height profile over 3 days with minimums set to zero.
Magnetometer. The magnetometer produced data with distinct spikes caused by various magnetic anomalies in the environment being travelled through. This can be seen in Figure 7 for the high-speed road.
Figure 7. Vertical axis magnetic field profile for a high-speed road.
Figure 8 is a zoomed-in section of the magnetometer data from the high-speed road in Figure 7. It shows correlation with an offset of approximately 44m between round 1 and round 3. This is mostly due to synchronization errors between the magnetometer counter and the GNSS receiver clock. This is the reason a second run of the road experiment was completed.
Figure 8. Zoomed-in section of the vertical axis magnetic field experienced on a high-speed road.
Microphone. The microphone was able to pick up clear signals when the vehicle was stationary, and the signal seems to be dependent on the speed of the vehicle. Figure 9 shows the profile from the microphone.
Figure 9. Profile from microphone attached to axle.
It may be possible to combine this data with the accelerometer or odometer data to develop a clearer picture of what sound is resulting directly from the road surface and what is speed related, although this still may not result in a useful feature for this study.
Thermometer. Temperature can vary particularly in a rural environment, seen in Figure 10. Similarities are not consistent across environments as seen from cross-correlation values in Table 2 and are likely to change with the seasons and due to weather conditions.
Figure 10. Temperature profile for rural roads.
Light Sensor. Four light sensors were used in the experiment: upwards, forwards, left and right facing. Figure 11 shows the data from the upward-facing sensor on the high-speed road. There are distinct events where the light level drops. Many of these instances correspond to gantries (bridge -like structures spanning highways displaying speed limits and other information). These features could be treated as discrete, whereby the sharp dips in light level would be treated as momentary events. Some of the information would be lost in treating the ambient light as discrete, but it would make the feature more robust against changing light levels due to shadowing or cloud cover.
If light is treated as a continuous feature, it can be seen in Table 2 that the cross correlation was inconsistent. This is partly due to the effects of changing light conditions. On the days with direct sunlight, the light sensor would reach its maximum intensity and be saturated. This can be seen in Figure 11, and this affects the cross-correlation coefficient calculated.
Table 2. Cross-correlation coefficients for sensor outputs for the four road types.Figure 11. The upward-facing light sensor profile for the high-speed road in second experiment day two.
Feature outcome. Thermometer data has been discounted as although it gave a cross-correlation coefficient of about 0.5 for the rural route, the other routes had lower cross-correlation values. Similarly, the microphone data had moderate success in high speed and rural environments but not in the other two routes. Therefore it will not be taken forward to the next phase although it could be used in the future if further processing was carried out on the data. As with the microphone, the light sensor had cross-correlation values greater than 0.5 in the rural and urban environments but had lower values in the other two. The success of this sensor is more reliant on the weather conditions than the environment type. At the current point this will not be brought forward to the next stage.
The accelerometer (used to measure road texture) showed no correlation with cross-correlation coefficients of approximately zero (between 0.0 and 0.3). It was useful for use in dead reckoning, but does not illustrate road texture.
The magnetometer and barometer showed the greatest potential for positioning with the highest cross-correlation values consistent over all the environments. These sensors are taken forward into phase 3.
DISCRETE FEATURES
A discrete feature is one where there are environmental events that occur at one position but can repeat multiple times along a route. The discrete feature can either be Boolean (an event occurs or does not) or it can be descriptive (different possible events or the strength of the event). Examples of discrete features include lamp posts, speed humps and shop signage.
In this paper, the discrete feature that will be discussed will be street signs, although the techniques used are applicable to many discrete features. How the signs are identified will also not be discussed in detail in this paper; instead, focus will be on how a sequence of discrete features is used show consistency across a route.
SCANNING METHOD
This section will look at scanning one round to find the region that best matches a region from a different round. Figure 12 illustrates this technique.
Figure 12. Diagram showing the principle used to scan for best match to a pre-set region.
The test data is scanned through the reference data. Cross-correlation coefficients are calculated as the test data is scanned through. The aim is to locate the position of the test data using the reference data for which the position is already known. The output of this exercise gives a cross-correlation profile (cross correlation as a function of position).
This profile can be treated similarly to a probability density distribution of position (although they are not the same) and so gives an idea of the probability of the position at each point in the test data.
Results. Two rounds from the suburban route are shown in this section as an example of the results achieved with the scanning method. Figure 13 and Figure 14 show the cross-correlation profiles for magnetic field and height for day 3 rounds 1 and 2 on the suburban route respectively. The test data region chosen is centered at 1.6 km into the route. The test data region size was 125 m for 4.5 km reference data.
It can be seen that the magnetic field has a number of peaks along the route. The peak with the highest cross-correlation coefficient is at the 1.6-km point (which is the correct position). For the height figure, there are many broad peaks at similar cross-correlation values approximately 700 m apart. The height peaks are broader than the magnetic peaks because the terrain height changes more slowly than the magnetic field.
Ambiguities, Dead Reckoning. The two graphs in Figure 13 and Figure 14 show that there are ambiguities present in both of the features. The majority of the features will have some ambiguities along a route, and so it is important to develop a technique that could mitigate them. One way ambiguities could be mitigated is by using the information available from dead reckoning. The dead-reckoning solution will have a specific position error (which grows with time), and the ambiguities from the features can be reduced by only considering the candidate position within the dead reckoning position uncertainty bounds.
Figure 13. Cross correlation scan for magnetic field on the suburban route with the center of the actual position of the test data shown as blue dotted line.
Figure 14. Cross correlation scan for the barometric height on the suburban route with the center of the actual position shown as blue dotted line.
COMBINING FEATURES
The quality of position information that can be extracted from a particular feature type varies with location. Thus, a better position solution can be obtained if higher weighting is attributed to higher quality features. Factors that will need to be considered include the precision of position that can be extracted from a feature, the level of ambiguity (Are there multiple candidate positions?) and the reliability (how much measurements vary unpredictably with time).
There are multiple ways to combine the scores from different features. Initially, there is the decision as to when in the position estimation process to combine the features. There are two ways to do this: Either combine the scores for each feature, or combine the position estimates for each sensor. The following subsections will describe a number of ways of combining the scores before estimating the position. It will be noted if these techniques could also be used to combine position estimates.
Equal Weighting. A simple combination technique is for each feature score to have equal weighting. The equal weighting used earlier took the two scores and found the average. This way, no single feature will dominate the navigation solution. As the feature scores are not probabilities, the values are not self-weighting, therefore it cannot be presumed that that equal weighting would always provide an optimal position estimation.
Test Data Weighting. This method takes a set of experimental data and empirically determines the weighting coefficients based on the best position solution in this test dataset. The test data would be used to maximize the score of the combined features using weighting at the correct position. This would have the benefit of using real data to determine the weighting, but its strength is based on how representative the test dataset is to the environments that the car will travel in.
Environment Weighting. This would detect the environmental context and use this to select an appropriate set of weights. For example, the presence of many Wi-Fi sources would suggest a suburban or urban environment, while a vehicle speed of 31m/s (70 mph/113 km/h) would suggest that the vehicle is likely to be on a highway. Based on this knowledge, it would possible to use a specific weighting coefficient set is developed for that environmental context.
Cross-Correlation Weighting. This weights each feature according to the characteristics of the cross-correlation coefficients profile obtained using the scanning method described earlier. This enables the weighing to adapt to the quality of the data. Figure 15 shows traits of a set of peaks that affect the confidence in the highest peak being the correct position.
Figure 15. Weighting nomenclature of actual position shown as blue dotted line.
Taking the uncertainty in the current position, only peaks that, for example, fall within 3 standard deviations would be evaluated. The characteristics of the tallest peaks compared with the others would be used to determine a measure of confidence for that feature.
There will be more confidence in the tallest peak (h0) if there is a greater difference between its height and that of the other peaks within the uncertainty range (h1, 2, 3). In Table 3 this is height.
Table 3. Cross-correlation profile weighting showing average distance from true position of the vehicle and the percentage of times the weighted scanning technique calculated the position within 100 m.
The next factor is the number of peaks within the uncertainty range (No. Peaks). The more peaks, the less confidence that the correct peak has been chosen as the position estimate.
The average cross-correlation coefficient within the uncertainty region (γ) would affect the confidence in the estimated position. If the average coefficient value (Av. CC) was similar to that of the highest peak, this suggests insufficient variation in the data being analyzed from that feature.
Finally, the standard deviation could be used. Calculating how many standard deviation (Std Dev) the highest peak was from the mean could provide a weighting value.
Each of these characteristics was looked at separately and compared against the benchmark of equal weighting using the scanning method comparing multiple pairs of rounds on different routes. It can be seen in Table 3 that the standard deviation from the mean provided the best weighting outcomes. To optimize the weighting algorithm, it may be that using a combination of these profile characteristics would provide the best position estimation.
Figure 16 and Figure 17 show examples of cross-correlation profiles; they show high and low confidence respectively. Figure 16 is the cross correlation of data from day three, rounds two and three, on suburban roads. It has a few spaced out peaks over the full profile, and one of the peaks is clearly higher than the others. Figure 17 is the cross correlation of data from day two, round three, and day three, round three, from the high-speed road. It has many similar height peaks all around the value of 0.5.
Figure 16. Good cross correlation profile; few spaced out peaks with one higher than all other peaks.Figure 17. Poor cross correlation profile; many low similar height peaks.
CONCLUSION
Environmental features have sufficient variability spatially and stability temporally for a database of features to be developed to create a map of the environment. This supports the hypothesis that it is feasible to map a space and then create a feature-mapping and navigation algorithm using a combination of environmental feature sensors, a GNSS receiver and sensors for dead reckoning.
FUTURE WORK
The next step of the project is to develop a feature-matching, mapping and navigation algorithm that incorporates inputs from the multiple sensors, a GNSS receiver, map-matching and sensors for dead reckoning. The algorithm will run collecting sensor data while GNSS receiver data is available, and store this in a database along with location stamps until called upon in times of GNSS receiver signal disturbance. The data from the road experiments will be used for a test database in developing the navigation system.
ACKNOWLEDGMENTS
Debbie Walter is funded by Engin-eering and Physical Sciences Research Council (EPSRC) and Terrafix ltd.
The authors thank Paul Neesham for a method of manually recording street signs seen in video footage and Juliusz Romaniuk of Terrafix for advice and creating hardware that contained the sensors’ carrier frequencies.
DEBBIE WALTER is a Ph.D. student at University College London in the Engineering Faculty’s Space Geodesy and Navigation Laboratory, and a software engineer at u-blox.
PAUL GROVES is a lecturer at UCL, where he leads a program of research into robust positioning and navigation. He holds a Ph.D. in physics from the University of Oxford.
BOB MASON is chief scientific officer and director of Terrafix Limited, holding a Ph.D. in communications and neuroscience from Keele University.
JOE HARRISON is principal radio frequency design engineer at Terrafix Ltd.
JOE WOODWARD is a software design engineer at Terrafix Ltd.
PAUL WRIGHT is a development engineer with Terrafix Ltd., with doctoral degrees in physics and electronics.
Detection of artillery blasts at a near distance (0.15 miles or 0.24 km) using a single infrasound sensor, with the sensor amplitude trace over time shown on Infiltec’s Amaseis software data and visualization package, and using some basic bandpass filtering (5 to 25 Hz). The spikes are clearly visible as high amplitude impulses in the traces, confirming sensor detection.
Infrasound refers to sound frequencies below the threshold of human hearing, around 20 Hz or less. There are a variety of natural sources of infrasonic emissions, including thunderstorms, avalanches, meteors, earthquakes, volcanos, and windstorms as well as manmade sources of emissions, such as aircraft, heavy machinery, artillery, missile testing and road traffic. Infrasound is especially attractive from a sensing perspective due to its ability to propagate long distances while suffering little from atmospheric or environmental attenuation.
Blasts detected at 5.22 miles (or 8.4 km) are still detectable, but additional signal processing or wind-filtering techniques may make these impulsive signals more prominent above the noise.
In this work, we describe the development of a man-portable “tactical” infrasound field sensor array that is small, lightweight and can be rapidly set-up and torn-down as needed. The system is able to provide direction-finding capabilities to infrasound impulse sources with a directional accuracy of +/–3 degrees. Such information could be used for alternative positioning schemes, described in detail, or perhaps for direction-finding (homing) to acoustic sources of interest. Possible users could be military or search-and-rescue teams operating in GPS-denied environments; field researchers studying volcanology or seismology; or other geo-acoustic scientists and engineers.
Taoglas, a provider of Internet of Things (IoT) and GNSS antenna products, has released two new GPS certification testing services for Google and its device partners. The services are required for devices to meet Google’s new Street View auto-ready standard.
Auto-ready certification distinguishes 360-degree cameras that deliver accurately positioned 360 video, even at high speeds. Taoglas worked with Google to develop the performance requirements, as well as the test methodology used to establish a basic minimum level of GPS receiver performance.
The services are available at any of Taoglas’ design centers and labs in the United States, Ireland, Germany and Taiwan.
Compact wireless devices such as digital cameras with built-in GPS receiver systems contain complex electronic systems that can emit unwanted RF signals that can impact radio receiver performance. The effect of this RF noise can be combated with critical design decisions like the antenna, low noise amplifier, filters, and transmission line choice and implementation.
Taoglas’ new services will help device manufacturers objectively measure real-world performance to understand any GPS performance issues with their products. With this information, product manufacturers will know if their performance is optimized and will meet or exceed user expectation for the application at hand, as well as how it compares with their competitors.
“Google Street View provides people with a 360-degree view of the world, and to enable these services, we require highly accurate location data,” said Charles Armstrong, product manager at Google. “By working with Taoglas to establish a standardized compliance process, we’re helping device manufacturers understand our requirements for GPS performance and quickly deliver products that match and exceed those high performance standards.”
Taoglas is offering two levels of certification testing:
Street View Auto-Ready Conformance Testing (GSA.31) provides a quick verification of minimum performance (in a pass/fail manner) required to achieve Street View certification. Taoglas uses its GPS constellation simulator and anechoic chamber to verify that radiated tracking and acquisition sensitivity meet a minimum performance standard at 15-degree intervals in one hemisphere.
From these test results, manufacturers will be able to clearly see if the device’s GPS is performing adequately for basic location capabilities. The condensed period needed to run this test provides device manufacturers the best value to answer the question, “Is the GPS working optimally?”
A street view image of Guatemala. (Credit: Google)
Street View Auto-Ready Performance Testing (GSA.32) provides an absolute level of testing to assess the GPS receiver performance according to the optional Google Street View Assessment test procedures.
Taoglas uses its GPS constellation simulator and anechoic chamber to measure radiated tracking and acquisition sensitivity at 15-degree intervals in one hemisphere. These optional tests provide more insight into how well a device performs, providing absolute receive sensitivity performance data.
Testing results for both services include suggestions on next steps to resolve identified issues.
“This partnership with Google to deliver GPS testing solutions for Google Street View compliance is an excellent example of how we’re working successfully with the world’s biggest companies to delivering high-quality, reliable antenna solutions,” said Dermot O’Shea, co-CEO of Taoglas. “By certifying their products through Taoglas, device manufacturers will also be able to take advantage of Taoglas’ deep RF expertise, achieving success quickly and reducing time to market.”
“Street view” of the Ambrym Volcano, Vanuatu. (Credit: Google)
TerraGo Magic, a custom app designed for both iOS and Android platforms, simplifies the process of designing a custom application for specific clients and needs.
With TerraGo Magic — now available in version 2.0 — an organization’s end users can rapidly build cloud-enabled iOS, Android and web apps, customized with their unique branding, workflow and features, without the expense of mobile software development, maintenance and operations.
Surveying firms can install the tool in their mobile devicew to enable the specific collection and sharing of important data that can vary as needed. This data can overlay Google and Apple Maps and allow attachments of images and video. Overall, the app avoids the time-consuming coding process, and could significantly improve work flow for many firms.
Distribution for the customized app is through the App Store for iOS and Play Store for Android.
“TerraGo Magic means we can assemble different apps with exactly the features the customer needs at the click of a button,” said Ben Chadbourne, project coordinator at Ameresco. “With the latest version, our end users have even more flexibility and visibility into the app they’re building. Not only can they turn features on and off, but they can preview the app instantly from the app studio, allowing them to publish a custom-built app without having developers build it from scratch.”
“TerraGo Magic is really about flipping the script on the app development backlog by enabling end users to assemble apps with proven features, exposed as configuration options in an easy-to-use interface,” said Dave Basil, vice president of product development at TerraGo. “It’s the power of the ‘write once, reuse many’ adage, but instead of limiting the user base to professional developers, we’ve extended it to enable masses of end users to build their own apps, creating a productivity play for the entire enterprise.”
TerraGo Magic features are operationally proven from a global customer base and field-tested across numerous industries for all types of workflows including data collection, mapping, asset management, inspection, survey, remote workforce management, dispatch, customer service, mobile forms, field reporting, advanced GIS, high-precision GPS and other field operations.
Register now for a GPS World webinar on May 25 to learn more and see a live demonstration of how TerraGo Magic can build a custom enterprise app from start to finish in minutes.
Oscilloquartz has launched the OSA 5405 SyncReach, an integrated PTP grandmaster and GNSS receiver with a patent-pending dual antenna and receiver to enable the mass roll out of small cells.
TerraGo Magic