Q: What is the optimum number of GNSS signals to include/process in a consumer-grade PNT device?
Daniel Ammann Executive Vice President u-blox Group
A: The cost for including additional silicon to a receiver for processing more signals is low, thanks to multiplexing hardware and high clock speeds. Having more satellite measurements allows the receiver to be selective about which ones it actually uses for PVT calculations, so a number of 30 or higher is desirable. Such a high number, and especially if the signals come from multiple constellations, enables the receiver to have a good view on integrity, too.
Gian Gherardo Calini Head of Market Development European GNSS Agency
A: The answer depends on application and environment where the device will be used. With the increasing need for ubiquitous positioning in difficult environments like urban canyons, the minimum number of satellites from one constellation is not sufficient. The technology makes it possible today to achieve better performance using multiple constellations with low impact on power consumption, and this is where we see the future.
Chaminda Basnayake Principal Engineer Renesas Electronics
A: Demand for more accuracy, availability, and reliability will drive design evolution. Sensor/map augmentations will likely drive system availability while depending on GNSS for better accuracy and reliability. As accuracy is a function of measurement quality and sky view — with the latter fixed for most use cases — placing more emphasis on minimizing errors appears ideal. Therefore, I see dual-constellation, dual-frequency GNSS as the optimal combination and the right balance between complexity versus performance.
Anticipating New, Different Application and User Needs
Users in emerging applications may have different requirements from traditional high-precision users. New users increasingly look to the technology not solely for position, but to navigate them through the environment, often autonomously or semi-autonomously. Tracking all of the new multi-GNSS signals, and then using the large number of inputs in the positioning engine, drives the amount of processing power and memory required onboard the receiver. These in turn drive the cost, size and power consumption of the receiver in exactly the opposite direction from the expectations of customers.
By Jason Hamilton
In considering the future of high-precision satellite navigation, we need to consider what users of the technology are trying to accomplish, and which growing and emerging applications will drive adoption of GNSS technology in the future. These applications will drive growth in our industry if we can correctly anticipate their future needs.
Traditional applications of high-precision GNSS are well understood, but what these customers have demanded from GNSS can be at odds with what users in emerging applications require. Survey and mapping users were early adopters of high-precision GNSS and remain large user segments. Surveying with GNSS requires the very best accuracy that GNSS can achieve. Every centimetre of accuracy matters. Power and size are important product attributes to survey manufacturers. Mapping customers increasingly are asking for not just position, but orientation of a camera or other sensors.
Once accuracy challenges were well in hand, the topic of availability came into play. It was no longer good enough to have an accurate position in open-sky situations. Applications demanded continuous positions that were accurate in more and more corner cases and challenging environments.
In addition to using GNSS to measure location in an environment, new applications are increasingly looking to the technology to navigate them through the environment — often autonomously, or semi-autonomously. For these users, whether operating on a farm, in a mine, on the ground, or in the air, position accuracy is only part of the requirement. Solution accuracy of course matters, but other receiver attributes such as real-time quality control and solution integrity monitoring, are equally or more important.
Multi-constellation, multi-frequency GNSS provides tremendous opportunity and also presents significant challenges for receiver manufacturers. Constellation and frequency support has previously been a differentiator among high-precision GNSS providers, and among product generations. The relative stability of the satellite constellation definition means that the signals broadcast from space will be relatively predictable for some time into the future, and as such, GNSS products are increasingly supporting “all in view,” the ability to track everything that is broadcast.
The benefits of more satellites, more frequencies (and resulting frequency combinations) and modern signal structures have been well publicized. As new and modernized GNSS constellations come on line, they will deliver more robust positioning in increasingly challenging environments such as urban centers, open-pit mines and under tree cover. We will be able to account for atmospheric effects more accurately, which will help during times of high ionospheric activity and extend the length of RTK baselines. Users have a great deal to look forward to from their next-generation receivers.
All of these improvements necessitate pretty dramatic changes in receiver design. Tracking four global constellations and numerous regional SBAS systems increases the complexity of tracking and positioning firmware and algorithms. Tracking multiple frequencies and signal types on each of these constellations drives the receiver channel count up substantially. The days of the 12-channel receiver are gone. Channels, typically implemented within the manufacturers’ custom chips, drive application-specific integrated circuit (ASIC) complexity, which drives cost, power consumption and physical size. Some of this can be mitigated through the use of smaller process geometries, embedded processors and peripherals, and RF chip integration; however, there are down-stream effects to all of these signals as well.
Challenges
Once your receiver has enough ASIC channels to track all-in-view, you need to do something with all that data. The receiver’s tracking sub-system generates code (pseudorange), carrier-phase and Doppler measurements for every signal on each satellite. With four global and multiple regional constellations and up to four frequencies on each satellite, that amounts to a great deal of data. These measurements are what we turn into position, through a range of different positioning algorithms from code positioning to real-time kinematic (RTK) to precise point positioning (PPP). Tracking all of these signals, and then using the large number of inputs in the positioning engine, drives the amount of processing power and memory required onboard the receiver. These in turn drive the cost, size and power consumption of the receiver in exactly the opposite direction from the expectations of customers.
Bandwidth. Communications bandwidth is also a future challenge. Positioning methods, such as RTK, that transmit base-station observations for each GNSS signal to field rover receivers, will require much more bandwidth in the all-in-view future. PPP, which provides a state-space correction of the underlying GNSS error sources, is a promising alternative to RTK that scales better with more satellites than RTK and provides performance that is good enough for many applications.
Utilizing the multiple frequencies available from modern constellations also presents challenges to receiver designers. RF designers are faced with the opposing challenges of making GNSS receivers and antennas smaller, lighter and lower cost, while also supporting more GNSS broadcast frequencies and mitigating against increasing amounts of interference in the L-band RF spectrum from non-GNSS uses. Robust RF design makes the difference between a system that works most of the time, and a system that works reliably all of the time.
Expectations
If we now come back to the expectations of end users, the challenges are clear. Most customers actually don’t care about all-in-view tracking, how many satellites are tracked, or about what the receiver is up to behind the scenes. Users will judge their GNSS receiver on whether or not they are receiving a position that meets the requirements of their application. Are they meeting their targets for accuracy, availability, latency, data rate, and does the receiver fit from a size, power consumption, regulatory and cost perspective? After a certain level, more observations do not make the solution more accurate or more robust. Manufacturers need to carefully manage the tradeoffs in their systems on behalf of users to produce the best quality position possible, while still meeting the customer expectations on all the other receiver attributes.
Sensor Fusion. Demands of new applications drive GNSS providers to consider more than just position. Most vehicle control applications require orientation information as well as highly accurate position. Multiple-antenna GNSS heading systems are becoming smaller than ever. Inertial measurement device technology is also evolving quickly. Miniature micro-electro-mechanical systems (MEMS) inertial sensors can now deliver performance that only a few years ago was exclusive to large, heavy, bulky systems. The integration of GNSS and inertial technologies has been well adopted in highly demanding applications like aerial and ground mapping. As the size, weight and cost of the technology continues to shrink, sensor fusion in many forms will become the standard for all machine control and autonomous vehicle applications.
Safety. This is a key consideration for system designers working on remotely or optionally piloted and autonomous systems. Position and orientation accuracy is important, but so, too, is assuring that the solution is right and can be trusted. The accuracy of the solution needs to be characterized in real time so that control systems can react as necessary to protect users on and around the vehicle. Often in these applications, accuracy can be traded off against the robustness and reliability of the solution. This presents new ways of thinking for firmware and algorithm developers who have focused for so long on solution accuracy.
Support. Lastly, let’s not forget having reliable supply of high-quality product, and expert customer service to back it up. As high-precision GNSS attracts new users in a range of new industries, they are less often geodesists or geomatics engineers. The products absolutely need to be easy to use correctly, backed up by complete and accurate product documentation and supported by world-class application engineers.
Jason Hamilton is vice president of marketing at NovAtel Inc. Since joining the company, he has held a number of research, development and product management roles. Jason holds a Bachelor of Science degree in geomatics engineering from the University of Calgary and an MBA from Royal Roads University.
SVN49 in space (artist’s rendering). The signal anomaly from SVN 49 alerted researchers to new possibilities in analysis and monitoring.
Chip Transition-Edge Based Signal Tracking for Ultra-Precise GNSS Monitoring Applications
By Sanjeev Gunawardena, John Raquet and Frank van Graas
Tracking GNSS signals using their underlying spreading sequence chip transition edges reveals positive versus negative chip asymmetries that are characteristic to each satellite. This asymmetry is due to various types of natural signal deformation that is known to occur within the satellite’s signal generation and transmission hardware. This novel concept of monitoring chip asymmetry can extend the state of the art in the areas of GNSS signal-quality monitoring and authentication. A technique to directly monitor chip asymmetry within a specially designed ChipShape GNSS receiver architecture employs separate code discriminators that align themselves to the chip rising-edge and falling-edge zero crossings.
The detailed study of naturally-present deformations in GNSS signals is a relatively new activity that was sparked by the GPS SVN49 anomaly and the associated research activities that followed. This research area has numerous applications that include:
Informing the design of sudden signal deformation detection and alerting algorithms for safety-of-life differential GNSS applications (such as aviation).
GNSS signal “fingerprinting” and authentication.
The detailed study of long-term degradation effects of GNSS satellite signal generation and transmission hardware.
Analysis of the impact to the first item in this list of swapping a satellite’s signal generation modules by its control segment.
Multipath detection, characterization, and mitigation are also closely tied to all research relating to GNSS signal deformation monitoring (SDM).
High-fidelity SDM can be performed using two methods:
observation of actual GNSS signals above the thermal noise floor using a high-gain dish antenna;
the combination of long coherent integration and multi-correlator processing.
Our previous research has revealed that these two methods are highly complementary for gaining full insight into the effects and causes of observed natural signal deformations.
Among the handful of multi-correlator processing techniques that can be applied for SDM, ChipShape processing allows the correlation function resolution to be finely adjustable while providing good numerical processing efficiency. This processing technique also allows chip-transition eye diagrams to be constructed in order to provide additional insight such as positive and negative chip width asymmetries.
One goal of our SDM research involves developing capabilities to observe GNSS signals with the highest levels of fidelity practically achievable in order to further the application areas described above. Key to this is developing techniques to track GNSS signals using a reference point that is both consistent and invariant (to the greatest extent possible) to nominal signal deformations and environmental effects such as multipath. Traditional multipath mitigating techniques such as narrow correlator and double-delta correlator are sub-optimal in this regard. This is because a significant portion of the signal around the chip transition point (that is, 10 percent and 20 percent for 0.1 chip correlator spacing, respectively) must be integrated to realize these discriminators and maintain robust tracking in moderate dynamics conditions. This integration tends to low-pass filter the desired observables.
Chip Transition Edge-Based Code Tracking
Figure 1 illustrates normalized C/A code chip rising edges for the GPS constellation of June 2014. These chip shapes were processed using a front-end with 24 MHz bandwidth. For visual comparison purposes, this and other related plots were obtained using 600 seconds of coherent integration.
Figure 1. Normalized ChipShape rising edges for the GPS SPS constellation of June 2014; each color represents a different GPS satellite.
The code tracking loop used to obtain this result employed an empirical normalized coherent rising-edge discriminator given by:
(1)
Where τ is relative code phase in chips, d is Early-Late correlator spacing,R’XYZ(i) is the differential correlation output for integer bin i obtained using ChipShape processing with masking sequence XYZ. bin(x) is a function that selects the closest ChipShape vector index that corresponds to relative code phase x. Each ChipShape processing bank is configured to span one chip early and one chip late with a resolution of N bins per chip, thus producing a ChipShape vector of 3N bins. α is a scale factor obtained through trial and error to yield robust tracking performance as observed by the code-minus-phase measurement. For the result shown in Figure 1, N=240 and d ≈ 0.017 chips.
The figure clearly shows that the rising-edge zero crossings vary by SV. This variation is due to nominal signal deformation present in each GPS-SPS signal.
Figure 2 illustrates the rising-edge zero crossings aligned to zero relative code phase. This alignment was performed by interpolating each R’NPN vector, precisely estimating code phase at the zero-crossing point, and shifting the curve appropriately.
Figure 2. Normalized ChipShape rising edges for the GPS SPS constellation of June 2014: Zero crossing compensated.
Figure 3 shows zero crossings for the falling edges after all rising edges were aligned to zero. The figure clearly illustrates subtle asymmetries between positive and negative chips which span a range of approximately ±1.5 meters. These asymmetries are not directly observable using typical GNSS receiver processing. However, they can lead to pseudorange biases through the resulting distortion that occurs to the traditional correlation function.
Figure 3. Normalized ChipShape falling edges for the GPS SPS Constellation of June 2014 when rising edges are aligned to zero.
In general, a family of code discriminators that precisely track chip rising-edge zero crossings can be defined by:
(2)
Where R’NPX is a linear combination of orthogonal ChipShape components that preserve the rising-edge transition, e.g.: R’NPX =R’NPN +R’NPP. R’FFX is a linear combination of orthogonal ChipShape components that preserve the non-transitioning (that is, flat) sections of chips, for example: R’FFX =R’PPP + R’PPN −R’NNP − R’NNN. a and b define an integration interval within the range −1 to +2 chips with respect to the chip transition edge. β is a bias compensation term. represents the real or imaginary component function for the coherent discriminator (depending on the modulation phase of the signal being tracked), or the magnitude function for a non-coherent discriminator implementation.
Similarly, a family of code discriminators that precisely track chip falling-edge zero crossings that occur one chip after the rising edges tracked by the discriminator of Equation 2 can be defined by:
(3)
Then, a two-step technique to precisely monitor chip asymmetry can be described as follows:
Setup two identical ChipShape processing channels to track a given PRN. Progressively tighten the code tracking loops to track the rising-edge zero crossings of the underlying signal using the discriminator of Equation 2.
After steady-state zero-crossing rising-edge tracking is achieved, switch the second channel’s code discriminator to that of Equation 3. This will cause the second channel to track the zero crossings of the falling edges that occur one chip later in the underlying signal’s spreading sequence. The discriminator’s linear range must be wide enough to pull-in the chip asymmetry shown in Figure 3.
When the second channel re-converges as a result of Step 2, the relative pseudorange displacement that occurs is equal to the chip asymmetry in meters. Hence, chip asymmetry can be monitored for the entire visible pass of a satellite. It is expected that positive and negative chip transitions are equally affected by channel distortions (that is, code and carrier multipath, ionosphere, troposphere, and the receiver antenna and front-end transfer function). Hence, the rising-edge-code-minus-falling-edge-code measure of chip asymmetry is expected to be invariant to most if not all channel distortions.
Estimating Compensation Parameters
As shown in Equations 2 and 3, due to natural signal deformation of many types, the rising and falling-edge zero-crossing discriminators are expected to be SV number, PRN code and elevation angle dependent. Hence, α and β must be estimated for a given correlator spacing d separately for all SV signals of the constellation. These values will also be specific to a given antenna and receiver front-end.
Figure 4 illustrates the procedure used to estimate the scale factor and bias terms starting with the empirical rising-edge tracking process described above.
Figure 4. Procedure for estimating scale factors and biases for rising-edge tracking early-late and double-delta code discriminators.
The following figures illustrate the edge tracking discriminator calibration process using R’NPN for a single SV.
Figure 5 illustrates the early-plus-late functions computed for various correlator spacings. As described previously, these functions typically do not cross through zero codephase due to natural signal deformation.
Figure 5. Uncorrected rising-edge early-late discriminator functions for various correlator spacings.
Figure 6 illustrates the rising-edge discriminator functions after bias compensation.
Figure 6. Rising-edge early-late discriminator functions for various correlator spacings after bias compensation.
Figure 7. Calibrated rising-edge early-late discriminator functions for various correlator spacings.
Figure 8 illustrates the multipath error envelopes for the rising edge-based coherent code discriminators. The performance of these discriminators is similar to the traditional Early-Late discriminators for the same correlator spacings. This result is consistent with the theoretical bounds for code multipath.
Figure 8. Multipath error envelopes for various rising edge-based coherent early-late code discriminator functions.
As shown in Figure 4, the edge-tracking discriminators described in Equations 2 and 3 that are based on Early-Late bin spacings can be combined to obtain edge-tracking double-delta discriminators. Double-delta discriminators provide significantly improved multipath performance.
In general, the edge-tracking double-delta discriminator for inner correlator spacing d is formed by the linear combination of two early-late edge-tracking discriminators, as follows:
(4)
Scale factor γ is estimated such that overall multipath error is minimized according to a given design criteria.
Figure 9 illustrates the double-delta rising-edge discriminator with inner spacing of 0.017 chips. This discriminator has a pull-in range of approximately ±0.01 C/A chips.
Figure 11 illustrates the multipath error envelope for the coherent rising-edge double-delta discriminator. Performance is consistent with a traditional second-derivative discriminator.
Figure 11. Multipath error envelope for coherent rising-edge double-delta code discriminator with inner spacing of ~0.017 C/A chips.
Figure 12 illustrates the performance of the various rising-edge tracking discriminators for a live-sky GPS-SPS signal (de-trended code-minus-carrier measurement). This figure clearly demonstrates robust code tracking and the multipath and noise mitigating benefit of ultra-narrow rising-edge discriminators.
Figure 12. Code tracking performance for live sky data of various rising edge-based coherent early-late code discriminator functions.
Conclusions
An empirical chip rising edge-based tracking technique was used to observe the underlying chip shapes of live sky GPS-SPS signals at high fidelity. These results reveal positive versus negative chip asymmetries that are characteristic to each satellite. The novel concept and technique of directly monitoring chip asymmetry has potential to extend the state of the art in the areas of GNSS signal quality monitoring and authentication.
Disclaimers. The views expressed in this paper are those of the authors and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the United States Government.
Acknowledgments. This research was supported by the Air Force Research Laboratory Sensors Directorate.
The authors thank Ohio University Avionics Engineering Center for making available a cluster of high-performance computers to process the 20 TB dataset for this research, and Kadi Merbouh of Ohio University for maintaining and overseeing operation of this equipment.
The ChipShape processing is an extension of the signal compression technique first published by Larry Weill and licensed by NovAtel for use in its Vision Correlator technology.
This article is based on a paper presented at ION Pacific PNT 2015 in Honolulu.
SANJEEV GUNAWARDENA is a research assistant professor with the Autonomy & Navigation Technology (ANT) Center at the Air Force Institute of Technology (AFIT). He earned a Ph.D. in electrical engineering from Ohio University.
JOHN RAQUET is a professor of electrical engineering and the Director of the ANT Center at AFIT. He has been involved in navigation-related research for more than 25 years.
FRANK VAN GRAAS is the Fritz J. and Dolores H. Russ professor of electrical engineering and principal investigator with the Avionics Engineering Center at Ohio University. He received the ION Johannes Kepler, Thurlow and Burka awards, and is a Fellow and past president of the ION.
By Steffen Thoelert, Johann Furthner, and Michael Meurer
Future positioning and navigation applications of modernizing and newly established GNSSs will require a higher degree of signal accuracy and precision. Thus, rigorous and detailed analysis of the signal quality of recently launched satellites, including the discovery of any possible imperfections in their performance, will have important implications for future users.
Global navigation satellite systems achieved amazing progress in 2012, with major milestones reached by the various navigation and augmentation systems, bringing new satellites and satellite generations into orbit. Since the complexity of the satellites and also the requirements for a precise and robust navigation increase consistently, all of the newly available signals of the existing or emerging navigation satellite systems must be analyzed in detail to characterize their performance and imperfections, as well as to predict possible consequences for user receivers.
Since the signals are well below the noise floor, we use a specifically developed GNSS monitoring facility to characterize the signals. The core element of this monitoring facility is a 30-meter high-gain antenna at the German Aerospace Center (DLR) in Weilheim that raises GNSS signals well above the noise floor, permitting detailed analysis. In the course of this analysis, we found differences in the signal quality in the various generations of the Chinese navigation satellite system BeiDou, differences which influence the navigation performance.
This article gives an overview of new navigation satellites in orbit. For selected satellites, a first signal analysis reveals important characteristics of these signals. The data acquisition of these space vehicles was performed shortly after the start of their signal transmission to get a first hint about the quality and behavior of the satellites.
For more detailed analysis, these measurements should be repeated after the satellites become operational. Then the acquired high-gain antenna raw data in combination with a precise calibration could be used for a wider range of analyses: signal power, spectra, constellation diagrams, sample analysis, correlation functions, and codes to detect anomalies and assess the signal quality and consequently the impact at the user performance.
Measurement Facility
In the early 1970s, DLR built a 30-meter dish (Figure 1) for the HELIOS-A/B satellite mission at the DLR site Weilheim. These satellite missions were the first U.S./German interplanetary project. The two German-built space probes, HELIOS 1 (December 1974–March 1986) and HELIOS 2 (January 1976–January 1981), approached the Sun closer than the planet Mercury and closer than any space probe ever. Later, the antenna supported space missions Giotto, AMPTE, Equator-S, and other scientific experiments.
Figure 1. 30-meter high-gain antenna.
In 2005, the Institute of Communications and Navigation of the DLR established an independent monitoring station for analysis of GNSS signals. The 30-meter antenna was adapted with a newly developed broadband circular polarized feed. During preparation for the GIOVE-B in-orbit validation campaign in 2008, a new receiving chain including a new calibration system was installed at the antenna. Based on successful campaigns and new satellite of modernizing GPS and GLONASS, and GNSSs under construction — Galileo and COMPASS — the facility was renewed and updated again in 2011/2012.
This renewal included not only an upgrade of the measurement system itself, but also refurbishment of parts of the high-gain antenna were refurbished.
The antenna is a shaped Cassegrain system with an elevation over azimuth mount. The antenna has a parabolic reflector of 30 meters in diameter and a hyperbolic sub-reflector with a diameter of 4 meters. A significant benefit of this antenna is the direct access to the feed, which is located within an adjacent cabin (Figure 2). The L-band gain of this high-gain antenna is around 50 dB, the beam width is less than 0.5°. The position accuracy in azimuth and elevation direction is 0.001°. The maximum rotational speed of the whole antenna is 1.5°/second in azimuth and 1.0°/second in elevation direction.
Figure 2. The shaped Cassegrain system: (1) parabolic reflector of 30 m diameter; (2) hyperbolic sub- reflector with a diameter of 4 meter; (3) sub-reflector; (4) Cabin with feeder and measurement equipment.
Measurement Set-up
The antenna offers another significant advantage in the possibility to have very short electrical and high-frequency connection between the L-band feeder and the measurement equipment. As mentioned earlier, the challenge for future GNSS applications is the high accuracy of the navigation solution. Therefore, it is necessary to measure and then analyze the signals very accurately and precisely. To achieve an uncertainty of less than 1 dB for the measurement results required a complete redesign of the setup, which consists of two main parts:
paths for signal receiving and acquiring the measurement data;
calibration elements for different calibration issues.
The path for receiving the signal and acquiring the measurement data consists of two signal chains, each equipped with two low-noise amplifiers (LNAs) with a total gain of around 70 dB, a set of filters for the individual GNSS navigation frequency bands, and isolators to suppress reflections in the measurement system. With this setup it is possible to measure right-hand circular polarized (RHCP) and left-hand circular polarized (LHCP) signals in parallel.
This provides the capability to perform axial ratio analysis of the satellite signal, and consequently an assessment of the antenna of the satellite. Using the switches SP01 and SP02, the measurement system is also able to acquire data from two different bands at the same time. This enabless investigations concerning the coherence between the signals in post-processing.
The signals are measured and recorded using two real-time vector signal analyzers with up to 120 MHz signal bandwidth. Both analyzers are connected to a computer capable of post-processing and storing the data. Additional equipment like digitizers or receivers can be connected to the system using the splitter III outputs, where the unfiltered RHCP signals are coupled out after the first LNA. A high-performance rubidium clock is used as reference signal for the whole measurement equipment. In front of the first LNA of each chain, a signal can be coupled in for calibration issues.
Control Software. Due to the distance of the antenna location from the Institute at Oberpfaffenhofen (around 40 kilometers) it was necessary to perform all measurement and calibration procedures during a measurement campaign via remote control. A software tool was developed which can control any component of the setup remotely. In addition, this software can perform a complete autonomous operation of the whole system by a free pre-definable sequence over any period of time. This includes, for example, the selection of the different band-pass filters, the polarization output of the feed, and the control of the calibration routines.
After the measurement sequence, the system automatically copies all data via LAN onto the processing facility, starts basic analysis based on spectral data, and generates a report. Sophisticated analysis based on IQ raw data is performed manually at this time.
Absolute Calibration
To fulfill the challenge of highly accurate measurements, it is necessary to completely characterize all elements of the measurement system, which comprises the antenna itself and the measurement system within the cabin after the feed. An absolutely necessary precondition of the calibration of the high-gain antenna is a very accurate pointing capability. The pointing error should be less than 0.01° concerning antennas of this diameter.
Furthermore, it is important to check long-term stability of these characterizations and the influences of different interference types and other possible error sources. This has to be taken in to account, when it comes to a point where the value of the absolute calibration has the same range as the summed measurement uncertainties of the equipment in use.
Antenna Calibration. High-accuracy measurements require not only the correct antenna alignment but also accurate power calibration of the antenna. To determine the antenna gain, well known reference sources are needed. These could be natural sources like radio stars or artificial sources like geostationary satellites.
Standard reference signal sources for the calibration of high-gain antennas are the radio sources Cassiopeia A, Cygnus, and Taurus. All these radio sources are circumpolar relative to our ground station, and therefore usable for calibrations at all times of the year. A further advantage of these calibration sources is the wide frequency range of the emitted signals. Thus, contrary to other signal sources (like ARTEMIS satellite L band pilot signal) the antenna gain can be calibrated in a wide bandwidth. With the help of the well-known flux density of the celestial radio sources and using the Y-method, the relation between the gain of the antenna and the noise temperature of the receiving system, or G/T, can be measured. Measuring the noise figure of the receiving system, the antenna gain can finally be calculated.
System Calibration. The measurement system calibration behind the feed is performed using wideband chirp signals. The chirp is injected into the signal chains via coupler I and II (Figure 3). The calibration signal is captured by the two vector signal analyzers. In the next step, the signal is linked via the switches directly to the analyzers, and the chirp signals are recorded as reference again. It has to be taken into account that more elements are in the loop during the chirp recordings compared to the receiving chain. These are the link between the signal generator and the couplers and the direct path to the analyzers.
Figure 3. Measurement setup overview.
To separate the receiving chain from the additional elements within the wideband calibration loop, two more measurements are needed. The injection path from the signal generator to the couplers and the direct paths are characterized by network analyzer (NWA) measurements. Based on the chirp and NWA measurements, the transfer function of the system is calculated to derive the gain and phase information. To determine the calibration curve over the frequency range from 1.0 GHz to 1.8 GHz, a set of overlaying chirps with different center frequencies is injected into the signal paths and combined within the analysis. Figure 4 and Figure 5 show the results of the wideband calibration of gain and phase.
Figure 4. Gain of the measurement system after the feed over 14 hours.Figure 5. Phase of measurement system.
Is it enough to determine the gain only once? If we assume that there is no aging effect of the elements, and the ambient conditions like temperature are constant, the gain should not change. In reality the behavior of the system is not constant. Figure 6 shows the temperature within the cabin during a failure of its air conditioning system. Figure 7 shows the corresponding gain of the measurement system during the temperature change in the cabin of about 5° Celsius. Clearly, it can be seen that the gain changed around 0.2 dB.
Figure 6. Cabin temperature increase during outage of the air condition concerning measurements shown in Figure 7.Figure 7. Gain variations of the measurement system based on temperature variations in the cabin (see Figure 6).
This example shows the sensitivity of the system to changes in environmental conditions. Usually the measurement system is temperature-stabilized and controlled, and the system will not change during data acquisition. But every control system can be broken, or an element changes its behavior. For this reason, the calibration is performed at least at the beginning and at the end of a satellite path (maximum 8 hours).
Measurement Results
Here we present selected results from the European Galileo and the Chinese BeiDou navigation systems.
Galileo FM3 and FM4. In October 2012, the third and fourth operational Galileo satellites, FM3 and FM4, were launched into orbit. Signal transmissions started in November and in December, respectively. Both satellites provide fully operational signals on all three frequency bands, E1, E5, and E6. The measurement data of both satellites were captured in December 2012, shortly after the beginning of the signal transmission. Figure 8 shows the spectra of both satellites for El, E5, and E6 bands. The quality of the transmitted signals seems to be good, but for the El signal of FM4 satellite, minor deformations of the spectra are visible.
Figure 8. Measurement results of Galileo IOV FM3 & FM4: El, E5 and E6 spectra.
Figure 9 shows the results of the IQ constellations both for FM3 and FM4 concerning each transmitted signal band. The constellations and consequently the modulation quality of each signal are nearly perfect for the FM3 satellite. The IQ constellation diagrams of FM4 show minor deformations in each band. What impact these imperfections create for future users has yet to be analyzed. Both satellites were at the time of measurement campaign still in the in-orbit test phase and did not transmit the final CBOC signal in the E1 band. It could be expected that especially the signals of the FM4 will be adjusted to become more perfect.
Figure 9 Measurement results of Galileo IOV FM3 & FM4: E1, E5, and E6 – IQ Constellation.
BeiDou M6. BeiDou satellites transmit navigation signals in three different frequency bands, all are located adjacent to or even inside currently employed GPS or Galileo frequency bands. The center frequencies are for the B1 band 1561.1 MHz, B3 band 1268.52 MHz, and B2 band 1207.14 MHz.
In 2012, China launched six satellites: two inclined geostationary space vehicles and four medium-Earth orbit ones, concluding in September (M5 and M6) and October 2012 (IGSO6). There have been further BeiDou launches in 2013, but these satellites’ signals are not analyzed here.
Figure 10 displays calibrated measurement results from the Beidou M6 satellite. The spectra of the B2 and B3 band of the Beidou M6 satellite are clean and show no major deformation. Within the B1 spectra, some spurious results, especially on top of the side lobes, are obvious. This behavior has to be investigated more in detail to determine their origin. The IQ diagrams, which visualize the modulation quality, show also no major deformation. Only within the B3 signal, a marginal compression of the constellation points can be seen, which points to a large-signal operation at the beginning of the saturation of the amplifier of the satellite.
Figure 10. BeiDou M6 satellite signal spectra and IQ constellations at B1, B2 and B3 band
Conclusion
Reviewing the quality of the presented measurements, signal analysis, and verification on GNSS satellites, the use of the 30-meter high-gain antenna offers excellent possibilities and results. Regarding the calibration measurements of the antenna gain and measurement system, the variances are in the range of measurement uncertainty of the equipment.
The sensitivity of the measurement system concerning ambient conditions was exemplarily shown based on the gain drift caused by a temperature drift. But the solution is simple: stabilize the ambient conditions or perform calibration in a short regular cycle to detect changes within the system behavior to be able to correct them.
Based on this absolute calibration, a first impression of the signal quality of Galileo FM3 and FM4 and the BeiDou M6 satellites were presented using spectral plots and IQ diagrams. Only minor distortion could be detected within the Galileo FM4 and Beidou M6 signal; these distortions may be negligible for most users. Concerning FM4 and FM3, both satellites were in the in-orbit test phase during the data acquisition. The signal quality may have been changed during their stabilization process in orbit, or the signals have been adjusted in the meantime. Thus, it would be interesting and worthwhile to repeat the measurements and perform detailed analysis to assess the final satellite quality and consequently the user performance.
Acknowledgments
The authors wish to thank the German Space Operation Centre for the opportunity to use the high-gain antenna. The support of colleagues at the DLR ground station Weilheim for the operational and maintenance service over recent years is highly appreciated. This work was partly performed within the project “Galileo SEIOT (50 NA 1005)” of the German Space Agency, funded by the Federal Ministry of Economics and Technology and based on a resolution by the German Bundestag. Finally, the support of DLR’s Centre of Excellence for Satellite Navigation is highly appreciated.
This article is based on the paper “GNSS Survey – Signal Quality Assessment of the Latest GNSS Satellites” presented at The Institute of Navigation International Technical Meeting 2013, held in San Diego, California, January 28–30, 2013.
Steffen Thoelert received his diploma degree in electrical engineering at the University of Magdeburg. He works in the Department of Navigation at German Aerospace Centre (DLR), on signal quality assessment, calibration, and automation of technical processes.
Johann Furthner received his Ph.D. in laser physics at the University of Regensburg. He works in the DLR Institute of Communication and Navigation on the development of navigation systems in a number of areas (systems simulation, timing aspects, GNSS analysis, signal verification, calibration processes).
Michael Meurer received a Ph.D. in electrical engineering from the University of Kaiserslautern, where he is now an associate professor, as well as director of the Department of Navigation at DLR.
An alternative tool for detecting underground nuclear explosions?
By Dorota A. Grejner-Brzezinska, Jihye Park, Joseph Helmboldt, Ralph R. B. von Frese, Thomas Wilson, and Jade Morton
Well-concealed underground nuclear explosions may go undetected by International Monitoring System sensors. An independent technique of detection and verification may be offered by GPS-based analysis of local traveling ionospheric disturbances excited by an explosion. Most of the work to date has been at the research demonstration stage; however, operational capability is possible, based on the worldwide GPS network of permanently tracking receivers. This article discusses a case study of detecting underground nuclear explosions using observations from GPS tracking stations and the Very Large Array radio telescope in New Mexico.
More than 2,000 nuclear tests were carried out between 1945 and 1996, when the Comprehensive Nuclear Test Ban Treaty was adopted by the United Nations General Assembly. Signatory countries and the number of tests conducted by each country are the United States (1000+), the Soviet Union (700+), France (200+), the United Kingdom, and China (45 each). Three countries have broken the de facto moratorium and tested nuclear weapons since 1996: India and Pakistan in 1998 (two tests each), and the Democratic People’s Republic of Korea (DPRK) in 2006 and 2009, and most recently, in 2013.
To date, 183 countries have signed the treaty. Of those, 159 countries have also ratified the treaty, including three nuclear weapon states: France, the Russian Federation, and the United Kingdom. However, before the treaty can enter into force, 44 specific nuclear-technology-holder countries must sign and ratify. Of these, India, North Korea and Pakistan have yet to sign the CTBT, and China, Egypt, Iran, Israel, and the United States have not ratified it.
The treaty has a unique and comprehensive verification regime to make sure that no nuclear explosion goes undetected. The primary components of the regime are:
The International Monitoring System: The IMS includes 337 facilities (85 percent completed to date) worldwide to monitor for signs of any nuclear explosions.
International Data Center: The IDC processes and analyzes data registered at IMS stations and produces data bulletins.
Global Communications Infrastructure: This transmits IMS data to the IDC, and transmits data bulletins and raw IMS data from IDC to member states.
Consultation and Clarification: If a member state feels that data collected imply a nuclear explosion, this process can be undertaken to resolve and clarify the matter.
On-Site Inspection: OSI is regarded as the final verification measure under the treaty.
Confidence-Building Measures: These are voluntary actions. For example, a member state will notifying CTBTO when there will be large detonations, such as a chemical explosion or a mining blast.
The IMS (see Figure 1) uses the following state-of-the-art technologies. Numbers given reflect the target configuration:
Seismic: Fifty primary and 120 auxiliary seismic stations monitor shockwaves in the Earth. The vast majority of these shockwaves — many thousands every year — are caused by earthquakes. But man-made explosions such as mine explosions or the North Korean nuclear tests in 2006, 2009, and 2013 are also detected.
Hydroacoustic: As sound waves from explosions can travel extremely far underwater, 11 hydroacoustic stations “listen” for sound waves in the Earth oceans.
Infrasound: Sixty stations on the surface of the Earth can detect ultra-low-frequency sound waves that are inaudible to the human ear, which are released by large explosions.
Radionuclide: Eighty stations measure the atmosphere for radioactive particles; 40 of them can also detect the presence of noble gas.
Figure 1. The International Monitoring System (IMS): worldwide facilities grouped by detection technologies used.
Only the radionuclide measurements can give an unquestionable indication as to whether an explosion detected by the other methods was actually nuclear or not. The observing stations are supported by 16 radionuclide laboratories.
Since radionuclide detection method provides the ultimate verification as far as the type of blast goes, it should be mentioned that while the 2006 North Korean event (yield of less than a kiloton) was detected by the IMS stations in more than 20 different sites within two hours of detonation, and both seismic signal and radioactive material were detected, the 2009 event (yield of a few kilotons) was detected by 61 IMS stations; seismic and infrasound signals were detected, but no radioactive material was picked up by the radionuclide stations. Seismic signal was consistent with a nuclear test, but there was no “ultimate” proof by the radionuclide method.
Thus, well-concealed underground nuclear explosions (UNEs) may be undetected by some of the IMS sensors (such as the radionuclide network). This raises a question: Is there any other technology that is readily available that can detect and discriminate various types of blasts, particularly those of nuclear type? Recent experiments have shown that an independent technique of detection and verification may be offered by GPS-based analysis of local traveling ionospheric disturbances (TIDs) excited by an explosion.
GNSS-Based Detection
Atmospheric effects from mostly atmospheric nuclear explosions have been studied since the 1960s.The ionospheric delay in GNSS signals observed by the ground stations can be processed into total electron content (TEC), which is the total number of electrons along the GNSS signal’s path between the satellite and the receiver on the ground. The TEC derived from the slant signal path, referred to as the slant TEC (STEC), can be observed and analyzed to identify disturbances associated with the underground nuclear explosion.
STEC signature (in spectral and/or spatial-temporal domains) can be analyzed to detect local traveling ionospheric disturbances (TID).
TID can be excited by acoustic gravity waves from a point source, such as surface or underground explosions, geomagnetic storms, tsunamis, and tropical storms. TIDs can be classified as Large-Scale TID (LSTID) and Medium-Scale TID (MSTID) based on their periods regardless of the generation mechanism. The periods of LSTIDs generally range between 30–60 minutes to several hours, and those of MSTIDs range from 10 to 40 or even 60 minutes. LSTIDs mostly occur from geophysical events, such as geomagnetic storms, which can be indicated by global Kp indices, while MSTIDs are genrally not related to any high score Kp indices. An underground nuclear explosion can result in an MSTID.
TIDs are generated either by internal gravity wave (IGW) or by acoustic gravity wave (AGW). The collisional interaction between the neutral and charged components cause ionospheric responses. The experimental results indicate IGWs can change the ozone concentration in the atmosphere. In the ionosphere, the motion of the neutral gas in the AGW sets the ionospheric plasma into motion.
The AGW changes the iso-ionic contours, resulting in a traveling ionospheric disturbance.
The past 10–15 years has resulted in a significant body of research, and eventually a practical application, with worldwide coverage, of GPS-based ionosphere monitoring. A significant number of International GNSS Service (IGS) permanent GNSS tracking stations (see Figure 2) form a powerful scientific tool capable of near real-time monitoring and detection of various ionospheric anomalies, such as those originating from the underground nuclear explosions (UNEs).
Figure 2. The IGS global tracking network of 439 stations.
The network is capable of continuously monitoring global ionospheric behavior based on ionospheric delays in the GNSS signals. The GNSS signals are readily accessible anywhere on Earth at a temporal resolution ranging from about 30 seconds up to less than 1 second.
A powerful means to isolate and relate disturbances observed in TEC measurements from different receiver-satellite paths is to analyze the spectral coherence of the disturbances. However, in our algorithms, we emphasize the spatial and temporal relationship among the TEC observations. Spatial and temporal fluctuations in TEC are indicative of the dynamics of the ionosphere, and thus help in mapping TIDs excited by acoustic-gravity waves from point sources, as well as by geomagnetic storms, tropical storms, earthquakes, tsunamis, volcanic explosions, and other effects.
Methodology of UNE Detection
Figure 3 illustrates the concept of the generation of the acoustic gravity wave by a UNE event, and its propagation through the ionosphere that results in a traveling ionospheric disturbance (TID). The primary points of our approach are: (1) STEC is calculated from dual-frequency GPS carrier phase data, (2) after eliminating the main trend in STEC by taking the numerical third order horizontal 3-point derivatives, the TIDs are isolated, (3) we assume an array signature of the TID waves, (4) we assume constant radial propagation velocity, vT, using an apparent velocity, vi, of the TID at the ith observing GNSS station, (5) since the TID’s velocity is strongly affected by the ionospheric wind velocity components, vN and vE, in the north and east directions, respectively, the unknown parameters,vT, vN, and vE, can be estimated relative to the point source epicenter, and (6) if more than six GNSS stations in good geometry observe the TID in GNSS signals, the coordinates of the epicenter can also be estimated.
Figure 3a. Pictorial representation of the scenario describing a GNSS station tracking a satellite and the ionospheric signal (3-point STEC derivative); not to scale.Figure 3b. The scenario describing a GNSS station tracking a satellite and the ionospheric signal and a point source (e.g., UNE) that generates acoustic gravity waves; not to scale.Figure 3c. The scenario describing a GNSS station tracking a satellite and the ionospheric signal, and the propagation of the acoustic gravity waves generated by a point source (e.g., UNE); not to scale.Figure 3d. The scenario describing a GNSS station tracking a satellite and the ionospheric signal, at the epoch when the GNSS signal is affected by the propagation of the acoustic gravity waves generated by a point source (e.g., UNE); not to scale.Figure 3e. Same as 3D, indicating that the geometry between GNSS station, the satellite and the IPP can be recovered and used for locating the point source; multiple GNSS stations are needed to find the point source location and the the velocity components of TID and ionospheric winds; not to scale.Figure 3f. Same as 3D, after the TID wave passed the line of sight between the GNSS stations and the satellite; not to scale.
Figure 4 illustrates the geometry of detection of the point source epicenter. Determination of the epicenter of the point source that induced TIDs can be achieved by trilateration, similarly to GPS positioning concept. The TIDs, generated at the point source, propagate at certain speed, and are detected by multiple GPS stations.
The initial assumption in our work was to use a constant propagation velocity of a TID. By observing the time of TID arrival at the ionospheric pierce point (IPP), the travel distance from the epicenter to the IPP of the GPS station that detected a TID (which is the slant distance from the ith station and the kth satellite) can be derived using a relationship with the propagation velocity. In this study, we defined a thin shell in the ionosphere F layer, 300 kilometers above the surface, and computed the IPP location for each GPS signal at the corresponding time epoch of TID detection.
Figure 4. Geometry of point source detection based on TID signals detected at the IPP of GPS station, i, with GPS satellite k. Unknown: coordinates of the point source, ( ф, λ ); three components of TID velocity vT, vN, and vE ; Observations: coordinates of IPP, (xik, yik, zik) and the corresponding time epoch to TID arrival at IPP, tik; Related terms: slant distance between IPP and UNE, sik; horizontal distance between the point source epicenter and the GPS station coordinates, di; azimuth and the elevation angle of IPP as seen from the UNE, αjk and εjk , respectively.
Very Large Array (VLA)
In addition to GNSS-based method of ionosphere monitoring, there are other more conventional techniques, for example, ground-based ionosondes, high-frequency radars, Doppler radar systems, dual-frequency altimeter, and radio telescopes. In our research, we studied the ionospheric detection of UNEs using GPS and the Very Large Array (VLA) radio telescope.
The VLA is a world-class UHF/VHF interferometer 50 miles west of Socorro, New Mexico. It consists of 27 dishes in a Y-shaped configuration, each one 25 meters in diameter, cycled through four configurations (A, B, C, D) spanning 36, 11, 3.4, and 1 kilometers, respectively. The instrument measures correlations between signals from pairs of antennas, used to reconstruct images of the sky equivalent to using a much larger single telescope. While conducting these observations, the VLA provides 27 parallel lines of sight through the ionosphere toward cosmic sources.
Past studies have shown that interferometric radio telescopes like the VLA can be powerful tools for characterizing ionospheric fluctuations over a wide range of amplitudes and scales. We used these new VLA-based techniques and a GPS-based approach to investigate the signature of a TID originated by a UNE jointly observed by both GPS and the VLA. For this case study, we selected one of the 1992 U.S. UNEs for which simultaneous GPS and VLA data were available.
Table 1. Characteristics of the analyzed events (UNEs).
Experimental Results
We summarize here the test studies performed by the OSU group in collaboration with Miami University and the U.S. Naval Research Laboratory on detection and discrimination of TIDs resulting from UNEs using the GNSS-based and VLA-based techniques. Table 1 lists the UNE events that have been analyzed to date. As of March 2013, the results of the 2013 North Korean UNE were not fully completed, so they are not included here.
In the 2006 and 2009 North Korean UNE experiments, STEC data from six and 11 nearby GNSS stations, respectively, were used. Within about 23 minutes to a few hours since the explosion, the GNSS stations detected the TIDs, whose arrival time for each station formulated the linear model with respect to the distance to the station. TIDs were observed to propagate with speeds of roughly 150–400 m/s at stations about 365 km to 1330 km from the explosion site. Considering the ionospheric wind effect, the wind-adjusted TIDs located the UNE to within about 2.7 km of its seismically determined epicenter (for the 2009 event; no epicenter location was performed for the 2006 event due to insufficient data). The coordinates estimated by our algorithm are comparable to the seismically determined epicenter, with the accuracy close to the seismic method itself. It is important to note that the accuracy of the proposed method is likely to improve if the stations in better geometry are used and more signals affected by a TID can be observed. An example geometry of UNE detection is shown in Figure 5.
Figure 5. Locations of the underground nuclear explosion (UNE) in 2009 and GNSS stations C1 (CHAN), C2 (CHLW), D1 (DAEJ), D2 (DOND), I1 (INJE), S1 (SUWN), S2 (SHAO), S3 (SOUL), U1 (USUD), Y1 (YANP), Y2 (YSSK) on the coastline map around Korea, China, and Japan. The TID waves are highlighted for stations C1, D1, D2, I1. The bold dashed line indicates the ground track for satellite PRN 26 with dots that indicating the arrival times of the TIDs at their IPPs. All time labels in the figure are in UTC.
For the Hunters Trophy and the Divider UNE tests, the array signature of TIDs at the vicinity of GPS stations was observed for each event. By applying the first-order polynomial model to compute the approximate velocity of TID propagation for each UNE, the data points — that is the TID observations — were fit to the model within the 95 percent confidence interval, resulting in the propagation velocities of 570 m/s and 740 m/s for the Hunters Trophy and the Divider, respectively.
The VLA has observing bands between 1 and 50 GHz, and prior to 2008 had a separate VHF system with two bands centered at 74 and 330 MHz. A new wider-band VHF system is currently being commissioned. The VHF bands and L-band (1.4 GHz) are significantly affected by the ionosphere in a similar way as the GPS signal. In this study, we used VLA observations at L-band of ionospheric fluctuations as an independent verification of the earlier developed method based on the GNSS TID detection for UNE location and discrimination from TIDs generated by other types of point sources.
The VLA, operated as an interfer-ometer, measures the correlation of complex voltages from each unique pair of antennas (baselines), to produce what are referred to as visibilities. Each antenna is pointed at the same cosmic source; however, due to spatial separation, each antenna’s line of sight passes through a different part of the ionosphere. Consequently, the measured visibilities include an extra phase term due to the difference in ionospheric delays, which translates to distortions in the image made with the visibilities. This extra phase term is proportional to the difference in STEC along the lines of sight of the two telescopes that form a baseline. Thus, the interferometer is sensitive to the STEC gradient rather than STEC itself, which renders it capable of sensing both temporal and spatial fluctuations in STEC.
The spectral analysis was performed on the STEC gradients recovered from each baseline that observed the Hunters Trophy event. Briefly, a time series of the two-dimensional STEC gradient is computed at each antenna. Then, a three-dimensional Fourier transform is performed, one temporal and two spatial, over the array and within a given time period (here ~15 minutes). The resulting power spectrum then yields information about the size, direction, and speed of any detected wavelike disturbances within the STEC gradient data.
Roughly 20 to 25 minutes after the UNE, total fluctuation power increased dramatically (by a factor of about 5×103). At this time, the signature of waves moving nearly perpendicular to the direction from Hunters Trophy (toward the northeast and southwest) was observed using the three-dimensional spectral analysis technique. These fluctuations had wavelengths of about 2 km and inferred speeds of 2-8 m s-1. This implies that they are likely due to small-scale distortions moving along the wavefront, not visible with GPS. Assuming that these waves are associated with the arrival of disturbances associated with the Hunters Trophy event, a propagation speed of 570–710 m/s was calculated, which is consistent with the GPS results detailed above.
In addition, a TID, possibly induced by the February 12, 2013, North Korean UNE, was also detected using the nearby IGS stations, by the detection algorithm referred to earlier. Eleven TID waves were found from ten IGS stations, which were located in South Korea, Japan, and Russia. Due to the weakness of the geometry, the epicenter and the ionospheric wind velocity were not determined at this point. The apparent velocity of TID was roughly about 330–800 m/s, and was calculated using the arrival time of the TID after the UNE epoch and the slant distance between the corresponding IPP and the epicenter. The reported explosion yield was bigger, compared to the 2009 North Korean UNE, which possibly affected the propagation velocity by releasing a stronger energy. However, more in-depth investigation of this event and the corresponding GPS data is required.
Conclusions
Research shows that UNEs disturb the ionosphere, which results in TIDs that can be detected by GNSS permanent tracking stations as well as the VLA. We have summarized several GNSS-based TID detections induced by various UNEs, and verified the GNSS-based technique independently by a VLA-based method using the 1992 U.S. UNE, Hunters Trophy. It should be noted that VLA observation was not available during the time of the Divider UNE test; hence, only the Hunters Trophy was jointly detected by GPS and the VLA. Our studies performed to date suggest that the global availability of GNSS tracking networks may offer a future UNE detection method, which could complement the International Monitoring System (IMS).
We have also shown that radio-frequency arrays like the VLA may also be a useful asset for not only detecting UNEs, but for obtaining a better understanding of the structure of the ionospheric waves generated by these explosions. The next generation of HV/VHF telescopes being developed (such as the Lower Frequency Array in the Netherlands, the Long Wavelength Array in New Mexico, the Murchison Widefield Array in Australia) utilize arrays of dipole antennas, which are much cheaper to build and operate and are potentially portable.
It is conceivable that a series of relatively economical and relocatable arrays consisting of these types of dipoles could provide another valuable supplement to the current IMS in the future, particularly for low-yield UNEs that may not be detectable with GPS.
Acknowledgment
This article is based on a paper presented at the Institute of Navigation Pacific PNT Conference held April 22–25, 2013, in Honolulu, Hawaii.
Dorota A. Grejner-Brzezinska is a professor and chair, Department of Civil, Environmental and Geodetic Engineering, and director of the Satellite Positioning and Inertial Navigation (SPIN) Laboratory at The Ohio State University.
Jihye Park recently completed her Ph.D. in Geodetic Science program at The Ohio State University. She obtained her B.A. and M.S degrees in Geoinformatics from The University of Seoul, South Korea.
Joseph Helmboldt is a radio astronomer within the Remote Sensing Division of the U.S. Naval Research Laboratory.
Ralph R.B. von Frese is a professor in the Division of Earth and Planetary Sciences of the School of Earth Sciences at Ohio State University.
Thomas Wilson is a radio astronomer within the Remote Sensing Division of the U.S. Naval Research Laboratory.
Yu (Jade) Morton is a professor in the Department of Electrical and Computer Engineering at Miami University.
