On November 18, a Consultation Event will take place in Brussels on the subject of receiver technology. The event is being held to inform the stakeholders of the European GNSS receiver community about the format and timeline of funding opportunities for the period 2015-2020, and to gather input for the definition of R&D actions in the field of receiver technology.
The workshop will consist of one panel session, during which stakeholders from industry, SMEs, academia, and technology institutes will be asked to debate and recommend important lines of research in receiver technology.
Registration is now open on the Europa website. Interested participants are invited to fill in the registration form and to indicate which application area they are interested in and the fields of research that should be supported.
The workshop will be held at the Committee of the Regions, Jacques Delors building, rue Belliard 99-101, room JDE 53, Brussels.
The United Nations is spotlighting the benefits of satellite navigation and its contribution to the betterment of humankind as part of the observance of World Space Week — an annual global celebration of the contributions of space science and technology to humanity.
The theme of this year’s World Space Week is Space: Guiding Your Way. It highlights the benefits of satellite navigation to society, which Simonetta Di Pippo, director of the UN Office for Outer Space Affairs (UNOOSA), said are of “great importance” to her office. UNOOSA also functions as the Executive Secretariat to the International Committee on Global Navigation Satellite Systems (ICG), which promotes voluntary cooperation on civil satellite-based positioning navigation, timing and value-added services.
Proclaimed by the UN General Assembly in 1999, World Space Week, observed each year during the week of October 4-10, aims to provide unique leverage in space outreach and education; educate people around the world about the benefits that they receive from space; encourage greater use of space for sustainable economic development; demonstrate public support for space programs; excite young people about science, technology, engineering, and math; and foster international cooperation in space outreach and education.
The dates recall the launch on October 4, 1957, of the first artificial satellite, Sputnik I, and the entry into force, on October 10, 1967, of the Treaty on Principles Governing the Activities of States in the Exploration and Use of Outer Space including the Moon and Other Celestial Bodies.
Ideas for educators and youth groups to focus on satellite navigation include geocaching, building model satellites, and using Google Earth. “Imagine a world without navigation satellites to guide planes, ships and cars and not to forget: us with our location-based mobile phone applications!” the guide states. “And navigation satellites not just accurately pinpoint our position on the planet, it also provides time signals to keep clocks in sync, which is critically important for global trading and many other time critical sectors. In times of disaster navigation satellites help rescuers quickly find spots where people need help. Using Geographic Information Systems (GIS) we can compare maps before and after things changed. And GNSS satellites are important to help you planning your trips and tell you where it will rain and where it will shine!”
Complexity and Context: Key Challenges of Multisensor Positioning
By Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London
The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Four key challenges must be met: complexity, context, ambiguity, and environmental data handling.
Although many new navigation and positioning methods have been developed in recent years to address GNSS shortcomings in terms of signal penetration and interference vulnerability, little has been done to bring them together into a robust, reliable, and cost-effective integrated system.
New positioning techniques investigated over the past 15 years include:Wi-Fi; ultra-wideband; phone signals; television and other signals of opportunity; Bluetooth; lasers, and dead reckoning; pedestrian dead reckoning (PDR) using step detection; pedestrian and activity-based map matching; magnetic anomaly matching; and GNSS shadow matching.
There have also been improvements to existing technologies: visual navigation, dead-reckoning algorithms, micro-electro-mechanical systems, inertial sensing with cold-atom technology, nuclear magnetic resonance gyros, distance-measuring equipment, Loran, Doppler with Iridium, multiple GNSS constellations, network assistance, and augmentation by commercial pseudolite systems.
In the next generation, a universal navigation system might be expected to provide position within 3 meters at any location with a very high reliability. No single positioning technology is capable of meeting the most demanding application requirements. Radio signals may or may not be subject to obstruction, attenuation, reflection, jamming, and/or interference. Known environmental features, such as signs, buildings, terrain height variation, and magnetic anomalies, may or may not be available for positioning. The system could be stationary, carried by a pedestrian, or on any type of land, sea, or air vehicle. Furthermore, for many applications, the environment and host behavior are subject to change. A multisensor solution is thus required.
A robust, reliable, and cost-effective integrated system must meet four key challenges:
Complexity. How to find the necessary expertise to integrate a diverse range of technologies, how to combine technologies from different organizations that wish to protect their intellectual property, how to incorporate new technologies and methods without having to redesign the whole system, and how to share development effort over a range of different applications.
Context. How to ensure that the navigation system configuration is optimized for the operating environment and host vehicle (or pedestrian) behavior when both are subject to change.
Ambiguity. How to handle multiple hypotheses, including measurements of non-unique environmental features, pattern-matching fixes where the measurements match the database at multiple locations, and uncertain signal properties, such as whether reception is direct or non-line-of-sight (NLOS).
Environmental Data Handling. How to gather, distribute, and store the information needed to identify signals and environmental features and define their points of origin or spatial variation.
Complexity
Achieving robust positioning in challenging environments potentially requires a large number of subsystems. For example, Figure 1 shows the possible components of a pedestrian navigation system using sensors found in a typical smartphone. Figure 2 shows possible components of a car navigation system using equipment already common on cars and other suitable low-cost sensors. Some technologies are common to the two platforms, while others differ.
Figure 1. Potential components of a pedestrian navigation system using smartphone sensors. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)Figure 2. Potential components of a car navigation system using commonly available equipment and other low-cost sensors. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)
Any multisensor navigation or positioning system needs integration algorithms to obtain the best overall position solution from the constituent subsystems. These algorithms must not only input and combine measurements from a wide range of subsystems, but also calibrate systematic errors in those subsystems. Designing the integration algorithms therefore requires expertise in all of the subsystems, which can be difficult to establish in a single organization. The more subsystems there are, the more of a problem this is.
The expert knowledge problem is compounded by the fact that different modules in an integrated navigation system are often supplied by different organizations, who may be reluctant to share necessary design information if this is considered to be intellectual property that must be protected. In a typical smartphone, one company supplies the GNSS chip, another supplies the Wi-Fi positioning service, a third organization supplies the mapping, the network operator provides the phone-signal positioning, a fifth company provides the inertial and magnetic sensors, and a sixth company produces the operating system. Because of lack of cooperation between these different organizations, useful information gets lost. For example, GNSS pseudo-range measurements are not normally available to application developers.
A further issue is reconfigurability. To minimize development costs, manufacturers share algorithms and software across different products, incorporating different subsystems. They also want to minimize the cost of adding new sensors to a product to improve performance. Similarly, researchers want to compare different combinations of subsystems. However, with a conventional system architecture, modifications must be made throughout the integration algorithm each time a subsystem is added, removed, or replaced. The more subsystems there are, the more complex this task becomes.
For a given application, different subsystems may also be used at different times. For example, a smartphone may use Wi-Fi positioning indoors and GNSS outdoors and may deploy different motion constraints and map matching algorithms, depending on whether the device is carried by a pedestrian or traveling in a car. Different integration algorithms for different configurations are more processor efficient, but also require more development effort. Conversely, an all-subsystem integration algorithm is quicker to develop, but can waste processing resources handling inactive subsystems.
Modular Integration. The solution to these problems is a modular integration architecture, consisting of a universal integration filter module and a set of configuration modules, one for each subsystem. The integration filter module would be designed by data fusion experts without the need for detailed knowledge of the subsystems. It would accept a number of generic measurement types, such as position fixes and pseudo-ranges, with associated metadata. The configuration modules would be developed by the subsystem suppliers and would convert the subsystem measurements into a format understood by the filter module and supply the metadata. They would also mediate the feedback of information from the integration filter to the subsystems. The metadata comprises the additional information required to integrate the measurements such as
the measurement type and any coordinate frame(s) used.
a sensor identification number (to distinguish measurements of the same type from different sensors).
statistical properties of the random and systematic measurement errors.
identification numbers and locations of transmitters and other landmarks.
A key advantage of this approach is that subsystems may be changed without the need to modify the integration filter. Provided the new subsystem is compatible, all that is needed is the corresponding configuration module.
Figure 3 shows an example of a modular integration architecture for a combination of conventional GNSS positioning, GNSS shadow matching, Wi-Fi positioning, and PDR. As well as providing measurements and associated statistical data to the integration filter module, the configuration modules feedback relevant information to the subsystems. Shadow matching works by comparing measured and predicted signal availability over a number of candidate positions, so requires a search area to be specified using other positioning technologies. PDR uses information from other sensors, where available, to calibrate the coefficients of its step length estimation model and correct for heading drift. Conventional GNSS positioning can also benefit from position and velocity aiding to support acquisition and tracking of weak signals in indoor and urban environments.
Figure 3. Modular integration of conventional GNSS, shadow matching, PDR, and Wi-Fi positioning for pedestrian navigation (different colors denote potentially different suppliers). (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)
In principle, each subsystem configuration module could simply supply a position fix to the integration filter module with an associated error covariance. However, other forms of measurement generally give better results. For conventional GNSS positioning, the advantages of tightly coupled (range- domain) integration over loosely coupled (position-domain) are well known.
PDR is a dead-reckoning technique, so measures distance traveled rather than position. Consequently, providing measurements of position displacement and direction can avoid cumulative errors in the measurement stream.
GNSS shadow matching and some types of Wi-Fi positioning use the pattern-matching positioning method. This scores an array of candidate position solutions according to the match between the measured and predicted signal availability or signal strength. Although the output of these algorithms is in the position domain, a likelihood distribution can provide more information for the integration filter than a simple mean and covariance.
Other navigation and positioning techniques generate further types of measurement, including velocity, attitude, specific force, angular rate, range rate, and bearings and elevations of features. The types of measurement depend on the positioning method.
A universal integration filter must operate without prior knowledge of which measurements it must process and which states it must estimate. Consequently, it must reconfigure its measurement vector, state vector, and associated matrices according to the measurements available, using the metadata supplied by the configuration module. This capability is sometimes called “plug and play,” and a number of prototypes have been developed by different research groups.
The integration filter must be capable of implementing either error-state or total-state integration, depending on the measurements available. In error-state integration, one of the subsystems, such as inertial navigation, provides a reference navigation solution. The integration filter estimates corrections to that solution using the measurements from other subsystems. In total-state integration, the integration filter estimates the position and velocity directly, and an additional configuration module provides information on the host vehicle (or pedestrian) dynamics.
Modular integration algorithms could form part of a wider modular integrated navigation concept in which subsystem hardware and software is shared across a range of applications.
Issues to Resolve
A critical requirement for the successful implementation of modular integration is an open-standard interface for communication between the universal filter and configuration modules. This enables modules produced by different organizations to work together. To realize the full benefits of modular integration, in terms of interoperability and software re-use, there should be a single standard covering the consumer, professional, research, and military user communities and spanning all of the application domains air, sea, land, indoor, underwater, and so forth. A standard developed by one group in isolation is unlikely to meet the needs of the whole navigation and positioning community, while the development of multiple competing standards defeats the main purpose of modular integration.
This interface should be defined in terms of fundamental measurement types, such as position, velocity, and the ranges, bearings, and elevations of signals and features. However, there are many different coordinate systems that may be used and positioning may be in 2 or 3 dimensions, while ranging measurements may be true ranges or pseudoranges. Ranging and angular positioning measurements may be differenced across transmitters or landmarks, differenced across receivers or sensors, or double differenced across both.
A universal interface must support every measurement type that requires different processing by the filter module. However, it need not support formats that are easily convertible. Thus, there is no need to support both the north, east, down, and east, north, up conventions. There are two main approaches to defining the fundamental measurement types:
A minimal number of very generic measurement types with metadata used to describe how these should be processed by the integration filter.
A large number of more specific measurement types for which the processing methodology is already known.
For each measurement type, an error specification must be defined. For error sources assumed to be white, a standard deviation or power spectral density (PSD) is required. For correlated errors, such as biases, information on the time correlation is required alongside variances and covariance information. The interface standard should include every conceivable error source. Unused errors can simply be zeroed. The filter module should then use the error specification to determine which error sources to model and how.
Obtaining reliable navigation sensor error specifications can be difficult. Manufacturers often provide only limited information, while performance in the field can be different from that in the laboratory due to vibration and electromagnetic interference. For new positioning techniques, the error behavior may not be fully understood, while complex error behavior can be difficult to measure. Adaptive estimation techniques provide only a partial solution. Even where the error behavior is well known, it can be too complex to practically model within the estimation algorithm. This could represent a fifth challenge.
For subsystems used as the reference in an error-state integration filter, such as an inertial navigation system (INS), the errors will typically be correlated across the different components of the subsystem navigation solution, for example position, velocity, and attitude. Furthermore, to represent the error behavior within an integration algorithm, it is necessary to model the error properties of the underlying sensors, accelerometers and gyroscopes in the case of inertial navigation. Thus, it is likely that additional compound measurement types for reference system data will be needed.
For pseudorange measurements, an issue to consider is the synchronization of different transmitter and receiver clocks. Clocks in receivers for different types of signal, such as GNSS and Loran, may or may not be synchronized with each other. Also, the transmitter clocks are typically synchronized in groups. For example, the GPS satellite clocks are synchronized with each other, as are the GLONASS satellite clocks, but GLONASS is not currently synchronized with GPS. For optimal integration of pseudoranges from different sources, this information must be conveyed to the integration filter.
The interface standard for communication between the filter and configuration modules must also support feedback of information from the integration filter to the subsystems, via the configuration modules. The integrated position, velocity, and attitude solution, with its associated error covariance, is useful for aiding many different subsystems. Therefore, a generic standard for this should be defined. Conversely, the feedback to the subsystems of calibration parameters estimated by the integration algorithm is sensor specific, so should be incorporated in the definitions of the fundamental measurement types.
The user requirements, such as accuracy, integrity, continuity, solution availability, update rate, and power consumption, can vary greatly between applications. For example, accuracy is important for surveying, integrity for civil aviation, solution availability for many military applications, and power consumption for many consumer applications. This impacts the design of the whole navigation system. Different modules could be used for different applications. However, it is more efficient if the components adapt to different environments. Figure 4 shows how requirements information can be disseminated in a modular integrated navigation system.
Figure 4. Modular integration architecture incorporating requirements. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)
An open-standard interface specification should be able to handle any conceivable navigation and positioning system. However, it is more efficient if the components adapt to different environments. Similarly, there will be differences in the error magnitudes that an integration filter can handle and in its capability to handle non-Gaussian error distributions. Variations in fault detection and integrity monitoring capability can also be expected. Consequently, there must be a capability specification for each filter module and a protocol for handling mismatches between the measurements and the filter module, and a means to certify that a filter module actually has the claimed capabilities. (Further discussion of modular integration may be found in our IEEE/ION PLANS 2014 paper, “The Four Key Challenges of Advanced Multisensor Navigation and Positioning,” and the Journal of Navigation paper, “The Complexity Problem in Future Multisensor Navigation and Positioning Systems: A Modular Solution.”)
Context
Context is the environment that a navigation system operates in and the behavior of its host vehicle or user. Examples include a pedestrian walking (behavior) in an urban street (environment), a car driving at highway speeds on an open road, and an airliner flying high above an ocean.
Context is critical to the operation of a navigation or positioning system. The environment affects the types of signals available. For example, GNSS reception is poor indoors while Wi-Fi is not widely available outside towns and cities. In underwater environments, most radio signals cannot propagate so acoustic signals are used instead. Processing techniques can also be context dependent. For example, in open environments, non-line-of-sight (NLOS) reception of GNSS signals or multipath interference may be detected using consistency checking techniques based on sequential elimination. However, in dense urban areas, more sophisticated algorithms are required and may be enhanced using 3D city models. GNSS shadow matching only works in outdoor urban environments.
Navigation using environmental feature matching is inherently context-dependent as different types of feature are available in different environments. Suitable algorithms, databases, and sensors must be selected. For example, terrain referenced navigation (TRN) uses radar or laser scanning in the air, sonar or echo sounding at sea, and barometric pressure on land. Map matching requires different approaches for cars, trains, and pedestrians. Similarly, algorithms and databases for image-based navigation depend on the types of feature available, which vary with the environment.
Behavioral context is also important and can contribute additional information to the navigation solution. For example, cars normally remain on the road, effectively removing one dimension from the position solution. Their wheels also impose constraints on the way they can move, reducing the number of inertial sensors required to measure their motion. Similarly, PDR using step detection depends inherently on the characteristics of human walking. Using PDR for vehicle navigation or vehicle motion constraints for pedestrian navigation will produce errors.
Host vehicle behavior is also important for tuning the dynamic model within a total-state navigation filter and for detecting faults through discrepancies between measured and expected behavior. Within a GNSS receiver, the behavior can be used to set tracking loop bandwidths and coherent correlator accumulation intervals, and to predict the temporal variation of multipath errors. The antenna placement on a vehicle or person can also affect performance.
Historically, context was implicit; a navigation system was designed to be used in a particular type of vehicle, handling its associated behavior and environments. However, many navigation systems now need to operate in a variety of different contexts. For example, a smartphone moves between indoor and outdoor environments and can be stationary, on a pedestrian, or in a vehicle. Similarly, a small surveillance drone may operate from above, amongst buildings, or even indoors. At the same time, most of the new positioning techniques developed to enable navigation in challenging environments, are context-dependent. To make use of these techniques in practical applications (as opposed to research demonstrators), it is necessary to know the context.
Context-Adaptive Navigation
The solution to the problem of using context-dependent navigation techniques in variable-context applications is context-adaptive navigation. As shown in Figure 5, the navigation system detects the current environmental and behavioral context and, in real time, reconfigures its algorithms accordingly. For example, different radio positioning signals and techniques may be selected, inertial sensor data may be processed in different ways, different map-matching algorithms may be selected, and the tuning of the integration algorithms may be varied.
Figure 5. A context-adaptive navigation system. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)
Previous work on context-adaptive navigation and positioning focused on individual subsystems and concerned either behavioral or environmental context, not both.
For example, there has been substantial research into classifying pedestrian motion using inertial sensors to enable PDR algorithms using step detection to estimate the distance travelled from the detected motion. The context information may also be used for non-navigation purposes.
Typically, orientation-independent signals are generated from the accelerometer and gyro outputs. Statistics such as the mean, standard deviation, root mean squared (RMS), inter-quartile range, mean absolute deviation, maximum−minimum, maximum magnitude, number of zero crossings, and number of mean crossings are then determined from a few seconds of data. Frequency-domain statistics may also be used. Finally, a pattern recognition algorithm is used to match these parameters to the stored characteristics of different combinations of activity types and sensor locations.
Detection of road-induced vibration using accelerometers has been used to determine whether or not a land vehicle is stationary, while a calibrated yaw-axis gyro can be used to determine when a vehicle is travelling in a straight line. Indoor and outdoor environments may be distinguished using GNSS carrier-power-to-noise-density ratio (C/N0 ) measurements. Wi-Fi signals might also be used for environmental context detection.
Context Detection Experiments
We have conducted a number of different context-detection experiments using GNSS, Wi-Fi, and accelerometers. Full details are presented in our ION GNSS+ 2013 paper, “Context Detection, Categorization and Connectivity for Advanced Adaptive Integrated Navigation,” and in our PLANS 2014 paper. Here, some highlights from the results are presented.
GNSS. GNSS data was collected at five locations inside and immediately outside UCL’s Grant Museum of Zoology; these are shown in Figure 6. C/N0 measurement data was collected from all GPS and GLONASS signals received by a Samsung Galaxy S3 Android smartphone. About 60 seconds of data was collected at each site. Figure 7 presents histograms of the C/N0 measurements and Table 1 lists the means and standard deviations.
Figure 6. Locations for the GNSS indoor/outdoor context detection experiment. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)Figure 7. GNSS C/N0 measurement distributions at sites inside and immediately outside UCL’s Grant Museum of Zoology. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)Table 1. Means and standard deviations of GNSS C/N0 measurements inside and outside UCL’s Grant Museum of Zoology. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)
As expected, the average received C/N0 is lower indoors than outdoors and lower deep indoors than near the entrance. Furthermore, the standard deviation of the C/N0 measurements is larger outdoors than indoors and also larger near the entrance to the building than deep indoors. Thus, both the mean and the standard deviation of the measured C/N0 across all GNSS satellites tracked are useful both for detecting indoor and outdoor contexts and for distinguishing between different types of indoor environment.
Indoor/Outdoor Detection, Wi-Fi. Tests in and around several UCL buildings have shown no clear relationship between Wi-Fi SNRs and environmental context. However, as the environment changes, there is a rapid change in the Wi-Fi SNRs over a few epochs. For a user moving from inside to outside of a particular building, those signals which originate inside go from strong to weak, while many of those from neighboring buildings become stronger. Consequently, Wi-Fi signals could potentially be used to detect context changes instead of the absolute context. This is useful for improving the overall robustness of context determination.
To test this, Wi-Fi data was collected using a Samsung Galaxy S3 smartphone along a route with both indoor and outdoor sections and a context-change score calculated from the last six epochs of data at 1-second intervals.
Context-change score results are presented in Figure 8. The large blue blocks indicate when the user was outside and the smaller blue block shows when the user was in the building’s basement, a very different Wi-Fi environment. As can be seen, there are clear peaks in the “context change” score whenever the user moves between indoor and outdoor contexts.
However, there are also peaks when the user enters and leaves the basement, so the technique is sensitive to false positives and must be combined with other context detection techniques to be used reliably.
Figure 8. Context-change score computer from Wi-Fi SNR measurements. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)
Behavioral Detection, Accelerometers. The use of accelerometers to detect behavioral context is well established. However, by looking at the vibration spectra, more information can be extracted. For these experiments, specific force data was collected using an Xsens MTi-G IMU/GNSS device, the mean subtracted to remove most of the gravity, and a discrete Fourier transform obtained using the MATLAB function fft. Figures 9 and 10 respectively show the vibration spectra of the specific force magnitude for an IMU on a table and held by a stationary pedestrian. The table spectrum is approximately white, whereas the pedestrian data shows peaks between 6 and 10 Hz.
Figure 9. IMU spectra on a table. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)Figure 10. IMU spectra, stationary pedestrian. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)
Figures 11 and 12 respectively show the vibration spectra of a stationary Vauxhall Insignia car, and a stationary urban electric train. Here, the individual accelerometer spectra are shown. In each case, the x-axis was pointing forward, the y-axis to the right and the z-axis down. The car exhibits a lot of vibration at frequencies above 10 Hz due to its engine, whereas the dominant train vibration peak is around 1.5 Hz, with smaller peaks at 15 Hz, 25 Hz, 33 Hz, and 50 Hz, the mains power frequency. Thus, the two vehicles are very different from each other and also from the pedestrian. Figure 13 then shows the vibration spectrum of the car moving on a high-speed road. As might be expected, there is much more vibration when moving with broad peaks below 15 Hz due to road vibration and above 15 Hz due to engine vibration.
Figure 11. Specific force frequency spectrum of a stationary car. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)Figure 12. Specific force frequency spectrum of a stationary train. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)Figure 13. Specific force frequency spectrum of a car traveling on a high- speed road. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)
Finally,Figure 14 shows the vibration spectra on an escalator at an underground rail station. The IMU was in the trouser pocket of a pedestrian. Vibration at a range of frequencies below 30 Hz can be seen and it was observed that the resonant frequencies vary between individual escalators.
Figure 14. Specific force frequency spectrum on an escalator. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)
Issues to Resolve
Despite the work done with individual sensors, a multisensor integrated navigation system that adapts to both environmental and behavioral context remains at the concept stage. Realizing this in a practical system requires both effective context determination and a set of context categories standardized across the whole navigation and positioning community.
The first step in the standardization process is to establish a framework suitable for navigation and positioning. Each context category must map to a configuration of the navigation system; otherwise, it serves no purpose. Multiple categories may map to the same configuration as different navigation systems will respond to different context information. In an autonomous context-adaptive navigation system, the context categories must also be distinguishable from each other.
Figure 15 shows the relationships in a five-attribute framework, comprising environment class, environment type, behavior class, vehicle type, and activity type. The environmental and behavioral contexts are treated separately because they perform fundamentally different roles in navigation. Environmental context concerns the availability of signals and other features that may be used for determining position whereas behavioral context is concerned with motion.
Figure 15. Proposed attributes of a context category. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)
Context may be considered at different levels. Sometimes it is sufficient to consider broad classes such as indoor or aircraft. In other cases, more detail is needed, specifying the type of indoor environment or the type of aircraft. Therefore, a two-level categorization framework, comprising class and type is proposed. The behavioral context comprises the vehicle type and the activity undertaken by that vehicle. A common set of classes containing separate vehicle and activity types is thus proposed. For pedestrian navigation, different parts of the body move quite differently, so the sensor location on the body is analogous to the vehicle type.
The broad classes of environmental and behavioral context are relatively obvious. We therefore propose that the community adopts the classes in Table 2. Standardization at the type level requires further research to determine:
which context categories a navigation system needs to distinguish between in order to optimally configure itself;
which context categories may be distinguished reliably by context detection and determination algorithms.
Table 2. Proposed environment and behavior classes. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)
Effective Context Determination. The reliability of current context detection techniques is typically 90−99%, with some context categories easier to detect than others. For the purposes of controlling a navigation system, this is relatively poor. Furthermore, context detection research projects have typically considered a much smaller range of context categories than a practical context-adaptive navigation system would need. Generally, the more categories there are, the harder it is to distinguish between them.
To make context determination reliable enough for context- adaptive navigation to be practical, a new approach is needed. Firstly, the context should be detected using as much information as possible, maximizing both the range of sensors used and the number of parameters derived from each sensor.
Environmental context detection experiments have largely focused on GNSS and Wi-Fi signals. Other types of radio signal; environmental features detected using cameras, laser scanners, radar, or sonar; ambient light; sounds; odors; magnetic anomalies, and air pressure could all be used. Context may also be inferred by comparing the position solution with a map, provided both are sufficiently accurate.
Behavioral context detection experiments have generally used inertial sensors. As shown earlier, this could be taken further by analyzing different frequency bands and, where possible, separating the forward, transverse, and vertical components. Other motion sensing techniques, such as visual odometry and wheel-speed odometry could be used. Context information, such as vehicle type, can also be determined from the velocity, attitude, and acceleration solutions.
Considering every combination of environment type, vehicle type (or pedestrian sensor location), and activity type produces potentially tens of thousands of different context categories — too many to practically distinguish using context detection techniques alone. However, the number of context categories that must be considered may be reduced substantially by using association, scope, and connectivity information, making the determination process much more reliable.
Association is the connection between the different attributes of context. Certain activities are associated with certain vehicle types and certain behaviors are associated with certain environments; an airliner flies, while a train does not, and flying takes place in the air, not at the bottom of the sea.
For a particular application, the scope defines each context category to be required, unsupported, or forbidden. This enables forbidden context categories to be eliminated from the context determination process and required categories to be treated as more likely than unsupported categories.
Connectivity describes the relationship between context categories. If a direct transition between two categories can occur, they are connected. Otherwise, they are not. Thus, stationary vehicle behavior is connected to pedestrian behavior, whereas moving vehicle behavior is not because a vehicle must normally stop to enable a person to get in or out. Context connectivity is directly analogous to the road link connectivity used in map matching and a similar mathematical formulation may be used. In practice, it is best to represent the connectivity as continuously valued transition probabilities rather than in Boolean terms. This facilitates recovery from incorrect context determination and enables rare transitions between context categories to be represented.
Location-dependent connectivity takes the concept a stage further by considering that many transitions between context categories happen at specific places. For example, people normally board and leave trains at stations and fixed-wing aircraft typically require an airstrip to take off and land. Thus context transition probabilities may be modeled as functions of the position solution, provided the positioning and mapping error distributions are adequately modeled and the probability of transitions occurring at unusual locations is considered.
Finally, for maximum robustness, the whole context determination process should be probabilistic, not discrete. The system should maintain a list of possible context category hypotheses, each with an associated probability. Multiple context detection algorithms should be used, each based on different sensor information. The detection algorithms should also output multiple context category hypotheses with associated probabilities. The context determination algorithm should then produce a new list of context category hypotheses and their probabilities by combining:
the previous list of hypotheses and their probabilities;
the hypotheses and probabilities output by the context detection algorithms;
context association, scope, and connectivity information.
Figure 16 illustrates the concept. When there is insufficient information to determine a clear context category, the list of context hypotheses and their probabilities will be output to the navigation algorithms. The handling of ambiguous information in navigation systems is discussed in Part 2.
Figure 16. Probabilistic context determination. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)
Context Adaptivity and Integration
The practical implementation of a complex multisensor navigation system for a multi-context application requires context-adaptive navigation to be incorporated into a modular multisensor integration architecture as described earlier. To enable different modules to adapt to changes in context, the architecture shown in Figure 4 should be extended to supply context information to the configuration modules, integration filter, and dynamic model from the system control module, alongside the user requirements. The configuration modules can then pass the context information onto the subsystems where necessary. Standardization of context categories and definitions across the navigation and positioning community is essential for this. Distribution of context information is useful even for single-context applications as it enables suppliers to provide modules that are optimized for multiple contexts.
The modular integration architecture must also support the context detection and determination process, allowing all subsystems to contribute. The configuration modules should therefore provide context detection information to a context determination module, as shown in Figure 17. The scope information should be supplied by the system control module.
Figure 17. Context-adaptive modular multisensor integration architecture. (Photo: Paul D. Groves, Lei Wang, Debbie Walter, Henry Martin, and Kimon Voutsis, University College London)
Potential architectures for this are discussed in our PLANS 2014 paper.
Ambiguity and Environmental Data
Part 2 of this article, appearing in the November issue, explores the two remaining key challenges and forms conclusions and recommendations.
Paul Groves is a lecturer at University College London (UCL), where he leads a program of research into robust positioning and navigation. He is an author of more than 50 technical publications, including the book Principles of GNSS, Inertial and Multi-Sensor Integrated Navigation Systems, now in its second edition. He is a Fellow of the Royal Institute of Navigation and holds a doctorate in physics from the University of Oxford.
Lei Wang is a Ph.D. student at UCL. He received a bachelor’s degree in geodesy and geomatics from Wuhan University. He is interested in GNSS-based positioning techniques for urban canyons.
Debbie Walter is a Ph.D. student at UCL. She is interested in navigation techniques not reliant on GNSS, multi-sensor integration and robust navigation. She has an MSci from Imperial College London in physics and has worked as an IT software testing manager.
Henry Martin is a Ph.D. student at UCL. His project is concerned with improving navigation performance from a low-cost MEMS IMU. He is interested in inertial navigation, IMU error modelling, multi-sensor integration and calibration algorithms. He holds a master of mathematics degree from Trinity College at the University of Oxford and an MSc in advanced mechanical engineering from Cranfield University.
Kimon Voutsis is a Ph.D. student at UCL. He is interested in pedestrian routing models, human biomechanics, and positioning sensor performance under high accelerations, particularly IMUs and GNSS. He holds an MSc in geographic information science (UCL). His Ph.D. project investigates the effects of pedestrian motion on positioning.
All authors are members of UCL Engineering’s Space Geodesy and Navigation Laboratory (SGNL).
A Look at High-Latitude and Equatorial Ionospheric Disturbances of GPS Signals
By Yu Jiao, Yu (Jade) Morton, Steve Taylor, and Wouter Pelgrum
INNOVATION INSIGHTS by Richard Langley
THE EARTH’S IONOSPHERE. It’s both a blessing and a curse. Together with the magnetosphere, it helps to protect life on our planet from the damaging outpour of particle and electromagnetic radiation from the sun. In particular, it absorbs a lot of the extreme-ultraviolet (EUV) radiation arriving at the Earth. In fact, that is primarily how the ionosphere is formed. The EUV energy strips off the outer electrons of atmospheric gases producing a plasma of free electrons and ions.
The ionosphere has another beneficial role in that it permits long distance radio communication using high-frequency (HF) or shortwave signals. Although its use is in decline since the advent of the Internet, HF is still in use by some broadcasters and military organizations and is indispensible during natural disasters when electricity grids and network links go down.
But the ionosphere can be a pain, too, particularly for GNSS users. The signals from GNSS satellites must travel though the ionosphere on their way to receivers on or near the Earth’s surface. The signals are perturbed by the presence of the free electrons causing an advance in the phase of a signal’s carrier and a delay in the arrival of the pseudorandom noise code modulation (due to the refractive index being frequency dependent or dispersive) and so there is a contribution to carrier-phase and pseudorange (code) measurements, which must be accounted for when determining positions, velocities, and time (PVT) from the measurements.
Again, since the ionosphere is a dispersive medium, by linearly combining simultaneous measurements (either pseudoranges or carrier phases) on two frequencies such as the GPS L1 and L2 frequencies, an observable virtually free of ionospheric effects can be constructed and used for PVT determinations. This approach does require, however, a dual- or multi-frequency receiver. Single-frequency receivers (or the post-processing of single-frequency data) require the use of a model to account for the ionospheric biases as much as possible. The GPS navigation message, for example, includes values of the parameters of a simple ionospheric model. But, on average, its accuracy is only around 50%. More accurate ionospheric corrections can be acquired from elsewhere, even in real time, such as those from satellite-based augmentation systems.
But there is another ionospheric effect that can play havoc with GNSS signals: scintillations. These are rapid fluctuations in the amplitude and phase of the signals caused by small-scale irregularities in the ionosphere. When sufficiently strong, scintillations can result in the strength of a received signal dropping below the threshold required for acquisition and tracking or in causing problems for the receiver’s phase lock loop resulting in many cycle slips.
The occurrence of scintillations depends on many factors including solar and geomagnetic activity, time of year, time of day, and geographical location. In particular, scintillations are most prevalent in equatorial and polar (Arctic and Antarctic) regions. And the processes involved are not fully understood, hindering our ability to model and predict scintillations.
In an effort to help improve the monitoring, mapping, and modeling of scintillations, a team of researchers led by Prof. Jade Morton is monitoring high-latitude and equatorial scintillations and they discuss some of their preliminary results in this month’s column.
“Innovation” is a regular feature that discusses advances in GPS technology and its applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick. He welcomes comments and topic ideas. Write to him at lang @ unb.ca.
Among other effects of the Earth’s ionosphere on GPS and other GNSS signals, scintillation is potentially the most problematic. Ionospheric scintillation refers to the random amplitude and phase fluctuations of radio signals after propagating through plasma irregularities. These irregularities occur more frequently in high-latitude and equatorial regions, especially during solar maxima. Occurrence of scintillation is difficult to predict and model because of the complexity of the ionosphere’s internal mechanisms and solar activities that are the driving forces of space weather phenomena. GNSS signals are particularly vulnerable to scintillation, as strong scintillation can severely impact the acquisition and tracking processes in GNSS receivers, causing degradation in positioning accuracy and even loss-of-lock. With the increasing reliance on GNSS applications, understanding the characteristics of ionospheric scintillation and its effects on GNSS signals and receivers has become an important topic and has gained worldwide attention from both ionospheric scientists and GNSS engineers.
Since 2009, our research group has established several ionospheric scintillation monitoring and data collection systems located in high-latitude and equatorial regions. The results presented here are based on data collected from a specialized commercial dual-frequency GPS ionospheric monitoring receiver at Gakona, Alaska (62.4°N, 145.2°W), and a commercial multi-system, multi-frequency GNSS ionospheric monitoring receiver located at Jicamarca, Peru (11.9°S, 76.9°W).
Measurements are filtered to remove slowly varying trends caused by satellite-receiver dynamics, receiver oscillator errors, the background ionosphere and troposphere gradient, and other potential contributions from multipath and man-made interferences. Scintillation events above preset threshold levels from the filter outputs are extracted for analysis. The threshold levels are set based on two commonly used scintillation indices, the S4 index and σφ , which are defined as the standard deviations of the detrended signal amplitude and carrier phase to represent the magnitude of signal intensity and phase fluctuation, respectively. In the study discussed in this article, the thresholds for S4 and σφ are 0.15 and 15°, respectively for high-latitude measurements. For low-latitude data, the threshold for S4 is raised to 0.2 to accommodate stronger amplitude scintillation, while the threshold for σφ remains 15°.
From data collected at Gakona, between August 2010 and March 2013, we extracted 655 amplitude and 2,355 phase-scintillation events from 657 equivalent days of data, while from data collected at Jicamarca, we extracted about 830 amplitude and 1,100 phase-scintillation events from 190 days of data collected from November 2012 to June 2013. Based on these events, we established a number of amplitude and phase scintillation distributions, which include scintillation-index-magnitude distributions, event-duration distributions, and event-occurrence frequency distributions. These results show very different characteristics of scintillation observed at low latitudes and high latitudes, indicating that there must be different mechanisms contributing to the formation and evolution of ionosphere plasma irregularities in the two regions. These characteristics are useful for scintillation-event prediction and modeling in the future.
Data Collection System and Event Thresholds
FIGURE 1 illustrates the general architecture of the event-driven GNSS data collection system. The system hardware consists of a multi-band GNSS antenna, a commercial ionospheric scintillation monitor (ISM) receiver, an array of reconfigurable software-defined radio (SDR) radio-frequency (RF) front-end devices capable of sampling intermediate-frequency (IF) signals, one or multiple data collection servers, a data storage array, timing signal distribution hardware to ensure both time and frequency consistency across all RF front ends and receivers, and network/communication devices that allow remote access of the receivers and servers to monitor the status of the hardware, to query recorded data, and reset and reconfigure the data collection system.
FIGURE 1. General architecture of the event-driven GNSS data collection system deployed at several high-latitude and equatorial sites since 2009.
Custom-designed space weather event monitoring and trigger software resides on the data collection and control server. The ISM receiver operates continuously to produce and record routine measurements such as I and Q channel accumulator outputs, pseudorange, carrier phase, Doppler frequency, C/N0, and scintillation indices, while the SDR RF front ends only temporarily store the latest one-minute worth of IF samples in each device’s circular buffer. Scintillation event thresholds are pre-determined based on analysis of baseline data collected at the same local site using the same hardware. The real-time event trigger software compares ISM receiver measurements with the pre-set event threshold. If the measurements exceed the thresholds, the contents of the circular buffers will be written to the data storage array until after the event subsides. These raw IF samples are then further post-processed using a wide range of receiver processing algorithms for analysis of scintillation features and robust receiver algorithm development.
The high-latitude GNSS receiver array at Gakona, was initially established in 2009 and has been continuously evolving into a four-antenna array capable of collecting GPS L1, L2C, and L5 and GLONASS L1 and L2 signal data until its recent relocation to and upgrade at Poker Flat Research Range, north of Fairbanks. Several publications have discussed the system setup, receiver signal processing of data collected by the system, and characterization of high-latitude scintillations based on analysis of the array outputs (see Further Reading). In this article, only the data collected using the commercial ISM receiver are discussed because this is the longest operating receiver at this site. The receiver outputs L1C/A signal intensity and carrier-phase measurements at a rate of 50 Hz and semi-codeless tracking results of L2P(Y) at 1 Hz.
Since 2011, several GNSS data collection systems have been deployed at low-latitude locations, including Hong Kong, Singapore, Peru, Ascension Island, and Puerto Rico. In this article, we use results from the ISM receiver at Jicamarca, Peru, close to the geomagnetic equator. FIGURE 2 shows the data-collection-system-setup block diagram at Jicamarca. The ISM receiver used in this location generates 100-Hz carrier-phase measurements and I/Q channel correlator outputs; the latter are further processed to generate 50-Hz signal-intensity measurements for GPS L1C/A, L2C, and L5 signals and GLONASS, Galileo, and BeiDou open signals. Seven SDR front ends driven by the same oven-controlled crystal oscillator (OCXO) signal from the ISM receiver sample GPS, GLONASS, Galileo, and BeiDou open signals. Preliminary results obtained from these and other low-latitude SDR data have been presented in several papers in the archived literature (see Further Reading).
FIGURE 2. Current multi-GNSS data collection system configuration at Jicamarca Radio Observatory in Peru. (GLO = GLONASS, BDS = BeiDou System, VPN = virtual private network, ISMET = ionospheric scintillation monitoring event triggering, RAID = redundant array of independent disks)
The raw carrier-phase and signal-intensity measurements obtained from the two ISM receivers at Gakona and Jicamarca were detrended, from which the two scintillation indices S4 and σφ were computed using Equations (1) and (2). In the two equations, I and φ stand for detrended signal intensity and carrier phase, respectively, and <·> represents the expected value that is essentially the average value over the interval of interest. In this study, the interval of interest was set to 10 seconds to most effectively highlight scintillation features based on evaluations of several different time intervals between 10 and 60 seconds.
(1)
(2)
As we mentioned earlier, the characterization of scintillation was carried out on the basis of scintillation events extracted from the raw data. After the evaluation of non-scintillation events and baseline indicators, a set of criteria has been established to extract interesting events through a semi-automated process from a large amount of data while keeping the number of selected events caused by non-scintillation factors (such as multipath and interference) low. A brief summary and explanations of the criteria are listed as follows:
The elevation angle mask is 30° to reduce multipath effects.
The thresholds for S4 and σφ are 0.15 and 15° respectively for data collected at Gakona.
For Jicamarca data, the thresholds are 0.2 and 15° respectively.
To exclude interference cases, the index value has to remain above the threshold value for a minimum of 30 seconds to qualify as a scintillation event.
An event detected within 5 minutes of the end of another event is combined as one event with the previous one.
Scintillations experienced by multiple satellite signals simultaneously are treated separately, and events experienced simultaneously for all visible satellites are further analyzed to ensure that they are not caused by interferences.
Carrier cycle slip/loss-of-lock detection and repair procedures are implemented to determine whether these cases are caused by scintillation or other factors.
It is important to note that the above criteria and procedures contain some degrees of arbitration, especially the last two, as they were applied based on visual inspections. These artificially imposed rules nevertheless are necessary for statistical analysis and comparison of scintillation observations.
Results and Discussion
In this section, we discuss the data sets we have collected and analyzed.
Available Dataset from Alaska and Peru. The ISM receiver at Gakona, started recording effective GPS data in August 2010. Environmental issues and human factors lead to a few intermittent data gaps during the more than three and a half years of data recording.
TABLE 1 lists monthly normal operation days and the percentage of time when data were collected. In all, the results presented in this article are based on approximately 3,000 scintillation events extracted from 657 days’ worth of data that was collected in a time span of 32 months.
Similarly, the number and percentage of days of effective data from Jicamarca, are summarized in Table 2. The dataset from this location runs from November 2012 until June 2013. Roughly 2,000 scintillation events have been extracted to enable statistical comparison of characteristics of scintillation observed in high- and low-latitude regions.
Scintillation Indicator Distributions. The magnitudes of the two scintillation indices, S4 and σφ , are often used to indicate the intensity of ionospheric scintillation, as their values directly reflect the disturbance rate of received power and carrier-phase measurements. Although there have been discussions regarding the suitability of σφ as a phase scintillation indicator, it is, nevertheless, a measure of the magnitude of carrier variations in a certain spectral range that are related to scintillation activities. In the absence of a commonly accepted new indicator for phase scintillation, we will use σφ in this study simply as a means to measure the phase fluctuations. FIGURE 3 compares the intensity distributions of amplitude and phase scintillation observed at the Alaska (square markers) and Peru (triangle markers) sites. MaxS4/σφ in the figures is the peak S4 or σφ value during an amplitude or phase scintillation event, which is a more practical indicator of scintillation impact on GNSS receivers.
FIGURE 3. Maximum S4 and σφ distributions of (a) amplitude and (b) phase scintillation observed at Gakona, Alaska, and Jicamarca, Peru.
Figure 3a shows that amplitude scintillation events observed at Jicamarca are generally more intense than those observed at Gakona. This is consistent with most previous studies, which concluded that scintillation is the most intense in the equatorial region. Figure 3b, on the other hand, shows that the intensity of phase scintillation at Jicamarca is slightly lower than that at Gakona. Nevertheless, this result does not necessarily reflect scintillation intensity observed in other parts of the equatorial region, as Jicamarca is not located close to the equatorial anomaly crest where scintillation activity is the strongest.
The duration of a scintillation event is another indicator of scintillation’s negative impact on the acquisition and tracking processes in receivers. FIGURE 4 plots the amplitude and phase event duration probability distributions, with the mean event durations at each site shown in the plots. The results show that at Gakona (square markers), phase scintillation lasts much longer than amplitude scintillation. At Jicamarca (triangle markers), amplitude scintillation events last slightly longer than the phase ones on average, and both types have much longer durations than those at high latitudes.
FIGURE 4. Duration distributions of (a) amplitude and (b) phase scintillation events observed at Gakona, Alaska, and Jicamarca, Peru.
Ionospheric scintillation of combined high intensity and long duration is usually considered a big threat to signal processing in GNSS receivers. Unfortunately, these two aspects are often correlated, especially at low latitudes. Moderate correlation coefficient values have been observed between scintillation durations and the magnitudes of scintillation indicators at Jicamarca (FIGURE 5b). The correlations, however, are much smaller at Gakona (FIGURE 5a), especially for amplitude scintillation events. These results further confirm that scintillation is a more severe issue in the equatorial region.
FIGURE 5. Scintillation duration vs. intensity at (a) Gakona, Alaska, and (b) Jicamarca, Peru.
Scintillation Occurrence Frequency and Relating Factors. We define the scintillation occurrence frequency as the number of scintillation events recorded during a certain time interval, which can be an hour, a day, a month, a season, and so on. The occurrence frequency is an important indicator in scintillation monitoring and forecasting, as it helps to identify the periods when scintillation events are most likely to occur.
FIGURE 6 illustrates scintillation hourly occurrence probabilities at the two sites with respect to Coordinated Universal Time (UTC) (upper) and hours post sunset (lower). Also consistent with numerous previous research findings, scintillation at high latitudes was more frequent during nighttime than at other times. Scintillation observed at Jicamarca occurred more frequently at night as well, but was greatly concentrated between one and two hours post sunset and midnight. Statistics show that 98% of Jicamarca’s scintillation events were observed from one to six hours after local sunset.
FIGURE 6. Scintillation occurrence frequency with respect to UTC hours and hours after sunset at (a) Gakona, Alaska, and (b) Jicamarca, Peru.
As demonstrated in Figure 6, scintillation occurrence frequency is largely influenced by solar inputs, which are the main driving force in atmospheric ionization and ionospheric irregularity formation. Scintillation occurrence can also be affected by geomagnetic activities. FIGURE 7 shows how scintillation occurrence frequency was affected by solar activity and seasons. The four seasons are defined as: spring (SP) – March to May; summer (SU) — June to August; fall (FA) — September to November; and winter (WI) – December to February. The intensity of solar activity is indicated by the smoothed average sunspot numbers, which are marked as black dots in the plot.
FIGURE 7. Seasonal scintillation occurrence frequency and smoothed sunspot number.
Several phenomena can be observed in Figure 7. At Gakona, scintillation occurrence frequency is clearly influenced by solar activity. The occurrence frequency is also modulated by season, with equinoxes generally more active than adjacent solstices. In contrast to the half-a-year cycle at high latitudes, scintillation occurrence frequency at Jicamarca more closely follows a one-year cycle as described in previous research, and decreases largely in the summer.
Our analysis also shows that the level of geomagnetic field activity also directly impacts scintillation occurrence frequency. FIGURE 8 shows the correlations between scintillation daily occurrence frequencies and Ap index values at the two sites. Ap is a widely used index that linearly reflects the daily average level of global geomagnetic field activity. Ap can be converted to the conventional Kp index using a quasi-logarithmic conversion table. The result in Figure 8a was obtained using data collected during seven months at Gakona: March and November 2011; March, July, October, and November 2012; and March 2013. During these months, scintillation activity was generally high. Figure 8b was generated using all the data listed in Table 2. Clearly shown in the plots, scintillation occurrence frequency at high latitudes is strongly correlated with geomagnetic field activities, while at Jicamarca such correlations do not exist. This result also confirms many previous research findings.
FIGURE 8. Daily scintillation occurrence frequency with respect to Ap index value at (a) Gakona, Alaska, and (b) Jicamarca, Peru.
Summary and Conclusions
This article presented comparative work on ionospheric scintillation characterization using data collected at Gakona, Alaska, and Jicamarca, Peru, during the current solar maximum to investigate the different natures of scintillation at high latitude and in equatorial regions. Scintillation intensity, duration, and occurrence frequency distributions were analyzed to demonstrate the differences at the two locations.
Scintillation in the equatorial region is typically more severe with deeper and faster signal power fadings and longer durations. Also, low-latitude scintillation with stronger intensity usually lasts longer, which further contributes to its negative impact on receivers. At high latitudes, phase fluctuations overwhelmed amplitude scintillation by the number of occurrences and their duration.
Scintillation is more frequent during nighttime, and almost all low-latitude scintillation events occur within six hours after local sunset. The overall occurrence frequency of scintillation not only increases with high solar activity, but also follows certain seasonal patterns. In general, scintillation is more active around the equinoxes. Additionally, high-latitude scintillation is also closely correlated to geomagnetic field activity, while the relationship is not obvious in the equatorial region.
Lastly, we would like to point out that the results presented here are preliminary and may be restricted to local effects, especially at low latitudes. As more data become available from Jicamarca and other equatorial sites where SDR data collection systems ensure quality inputs during strong scintillation events, a more comprehensive analysis and comparison can be made to facilitate global scintillation monitoring, mapping, and modeling.
Acknowledgments
The data collection and analysis project discussed in this article was supported by the U.S. Air Force Office of Scientific Research and Air Force Research Laboratory grants. The authors appreciate the support of High Frequency Active Auroral Research Program (HAARP) staff and the University of Alaska Fairbanks Geophysical Institute for organizing and sponsoring the HAARP campaign and HAARP staff support of the GNSS receiver data collection system setup. The authors would also like to acknowledge Jicamarca Radio Observatory for hosting the GNSS equipment. The Jicamarca Radio Observatory is a facility of the Instituto Geofisico del Peru, operated with support from the U.S. National Science Foundation through Cornell University. This article is based, in part, on the paper “Comparative Studies of High-latitude and Equatorial Ionospheric Scintillation Characteristics of GPS Signals” presented at PLANS 2014, the Institute of Electrical and Electronics Engineers / Institute of Navigation Position, Location and Navigation Symposium held in Monterey, California, May 5–8, 2014.
Manufacturers
The commercial ISM receivers used at Gakona and Jicamarca were a GPS Silicon Valley — now NovAtel Inc. — GSV4004B and a Septentrio N.V. PolaRxS Pro, respectively.
YU JIAO is a Ph.D. candidate at the Colorado State University (CSU), Fort Collins, Colorado. She received her master’s degree in computational science and engineering from Miami University, Oxford, Ohio, in 2013 and her bachelor’s degree in electronic and information engineering from Beihang University (previously known as the Beijing University of Aeronautics and Astronautics), Beijing, China, in 2011. Her research interests are in GNSS signal processing and ionosphere effects on GNSS in both high-latitude and equatorial regions.
YU (JADE) MORTON is an electrical engineering professor at CSU. She received a Ph.D. in electrical engineering from Pennsylvania State University (Penn State), State College, Pennsylvania, and was a post-doctoral research fellow in the Space Physics Research Laboratory of the University of Michigan, Ann Arbor, Michigan. Prior to joining CSU, she was a professor in the Department of Electrical and Computer Engineering at Miami University. Her research interests are advanced GNSS receiver algorithms for accurate and reliable operations in challenging environments, studies of the atmosphere using radar and satellite signals, and development of new applications using satellite navigation technologies.
STEVE TAYLOR is a graduate student in the Department of Electrical and Computer Engineering at Miami University. He received his B.S. in computer science from Miami University in 2011. Taylor developed software systems for ionosphere space weather monitoring and has been involved in deployment of Dr. Morton’s research team’s GNSS data collection system in Alaska, Peru, Hong Kong, Ascension Island, and Puerto Rico.
WOUTER PELGRUM is an assistant professor of electrical engineering at Ohio University, where he conducts research in and teaches about topics in electronic navigation, such as GNSS, Distance Measuring Equipment or DME, and time and frequency transfer. Before joining Ohio University in 2009, he worked in private industry, where he contributed to the development of an integrated GPS-eLoran receiver and antenna. From 2006 until 2008 he operated his own company, specializing in navigation-related research and consulting.
FURTHER READING
• Authors’ Conference Paper
“Comparative Studies of High-latitude and Equatorial Ionospheric Scintillation Characteristics of GPS Signals” by Y. Jiao, Y. Morton, and S. Taylor in Proceedings of PLANS 2014, the Institute of Electrical and Electronics Engineers / Institute of Navigation Position, Location and Navigation Symposium, Monterey, California, May 5–8, 2014, pp. 37–42, doi: 10.1109/PLANS.2014.6851355.
• Introduction to Ionospheric Scintillation and GNSS
“Limitations in GPS Receiver Tracking Performance Under Ionospheric Scintillation Conditions” by S. Skone, K. Knudsen, and M. de Jong in Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy, Vol. 26, No. 6-8, 2001, pp. 613–621, doi: 10.1016/S1464-1895(01)00110-7.
“Radio Wave Scintillations in the Ionosphere” — a review paper by C.K. Yeh and C.-H. Liu in Proceedings of the IEEE, Vol. 70, No. 4, 1982, pp. 324–360, doi: 10.1109/PROC.1982.12313.
High-Latitude Scintillations
“Characterization of High Latitude Ionospheric Scintillation of GPS Signals” by Y. Jiao, Y. Morton, S. Taylor, and W. Pelgrum in Radio Science, Vol. 48, 2013, pp. 698–708, doi: 10.1002/2013RS005259.
Equatorial Scintillations
“Statistics of GPS Scintillations over South America at Three Levels of Solar Activity” by A.O. Akala, P.H. Doherty, C.E. Valladares, C.S. Carrano, and R. Sheehan in Radio Science, Vol. 46, No. 5, October 2011, doi: 10.1029/2011RS004678.
“Measuring Ionospheric Scintillation in the Equatorial Region over Africa, Including Measurements from SBAS Geostationary Satellite Signals” by A.J. Van Dierendonck and B. Arbesser-Rastburg in Proceedings of ION GNSS 2004, the 17th International Technical Meeting of the Satellite Division of The Institute of Navigation, Long Beach, California, September 21–24, 2004, pp. 316–324.
“An Improved Ionosphere Scintillation Event Detection and Automatic Trigger for GNSS Data Collection Systems” by S. Taylor, Y. Morton, Y. Jiao, J. Triplett, and W. Pelgrum in Proceedings of ION ITM 2012, The Institute of Navigation 2012 International Technical Meeting, Newport Beach, California, January 30 – February 1, 2012, pp. 1563–1569.
Software Defined Radio Processing of GPS Scintillation Data
“Triple Frequency GPS Signal Tracking During Strong Ionospheric Scintillations over Ascension Island” by M. Carroll, Y.J. Morton, and E. Vinande in Proceedings of PLANS 2014, the Institute of Electrical and Electronics Engineers / Institute of Navigation Position, Location and Navigation Symposium, Monterey, California, May 5–8, 2014, pp. 43–49, doi: 10.1109/PLANS.2014.6851356.
Forecasting Scintillations
“A Forecasting Ionospheric Real-time Scintillation Tool (FIRST)” by R.J. Redmon, D. Anderson, R. Caton, and T. Bullett in Space Weather, Vol. 8, No. 12, December 2010, doi: 10.1029/2010SW000582.
“Specification and Forecasting of Scintillations in Communication/Navigation Links: Current Status and Future Plans” by S. Basu, K.M. Groves, Su. Basu, and P.J. Sultan in Journal of Atmospheric and Solar-Terrestrial Physics, Vol. 64, 2002, pp. 1745–1754, doi: 10.1016/S1364-6826(02)00124-4.
Alternative Scintillation Indices
“Improved Amplitude- and Phase-scintillation Indices Derived from Wavelet Detrended High-latitude GPS Data” by S.C. Mushini, P.T. Jayachandran, R.B. Langley, J.W. MacDougall, and D. Pokhotelov in GPS Solutions, Vol. 16, No. 3, July 2012, pp. 363–373, doi: 10.1007/s10291-011-0238-4
“Perils of the GPS Phase Scintillation Index (sf)” by T.L. Beach in Radio Science, Vol. 41, RS5S31, 2006, doi: 10.1029/2005RS003356.
“Problems in Data Treatment for Ionospheric Scintillation Measurements” by B. Forte and S.M. Radicella in Radio Science, Vol. 37, No. 6, 1096, 2002, pp. 8-1–8.5, doi: 10.1029/2001RS002508.
JAVAD GNSS has published a chart showing that it has tracked the IRNSS (Indian Regional Navigational Satellite System) L5 signal.
Shortly after the Indian Space Research Organization (ISRO) released its IRNSS Signal in Space Interface Control Document (ICD), JAVAD GNSS was able to track the L5 BPSK signal from both 1A and 1B satellites. Ability to track IRNSS L5 will be added to all JAVAD L5-capable receivers in the near future, the company said.
SNR of two passes of 1A satellite (IGSO) over Moscow.
The Jupiter SL869-V2S GPS module. Photo: Telit Wireless Solutions
Telit Wireless Solutions, a global provider of high-quality machine-to-machine (M2M) modules and services, today debuted the Jupiter SL869-V2S GPS module, designed for easy migration between a full-GNSS solution for top-ranked applications and a simple GPS-only solution for less demanding applications.
The Jupiter SL869 V2S supports GPS as well as QZSS constellations and is ROM based. Geo-location data is delivered using NMEA protocol through a standard UART port. It supports ephemeris file injection (A-GPS) as well as Satellite Based Augmentation System (SBAS) for increased position accuracy. Its onboard software engine is able to locally predict ephemeris three days in advance starting from ephemeris data broadcast by GNSS satellites, received by the module and stored in the host flash memory.
Key benefits include:
Pin-to-pin compatibility with JN3/xL869 family
Same protocol used in SL869 V2
Straightforward migration between full-GNSS solutions and GPS-only solutions
SBAS support, for increased position accuracy
Assisted GPS
The SL869 V2S can replace the JN3, SL869 or SL869 V2 — allowing customers to design once and interchangeably mount the appropriate solution depending on the required features. The xL869 is Telit’s GNSS unified form-factor family, which allows customers to select among different GNSS technologies and feature sets. Modules in this family are offered in a 16 x 12.2 mm, 24-pad, LCC package.
“The new SL869 V2S module is designed to be easily swapped with other xL869 modules for enhanced simplicity and scalability,” said Taneli Tuurnala, CEO of Telit GNSS Solutions. “It is an ideal example of how buying a module from Telit enables our customers to avert the need to keep track of the latest chipset technology on their own. We keep them on top of the best available technology, pre-packaged in a module that is easy to replace as needed, without having to redesign their entire application to stay up to date.”
IFEN has launched the SX3 receiver. The company’s previous scientific software receiver, the SX‐NSR, was subject to major upgrades, while the respective hardware front-end was completely redesigned. Together, they build the new SX3 GNSS software receiver.
IFEN’s most important innovation of the year was introduced at the ION GNSS+ Conference September 9-12 in Tampa, Florida.
One of the SX3’s key features is four RF frequency bands, which can be split into a maximum of eight sub-bands per unit. This enhances the bandwidth to a full 55 MHz per RF band, offering additional signal power, especially in E5 band. The new USB 3.0 port empowers a unrivaled data transfer rate that makes a maximal bit-quantization of up to 8-bit possible — for every single stream.
The additional power is compressed into a significantly smaller and lighter hardware chassis than before. Among other options, a dual antenna-input feature can be ordered as well as an OXCO clock. (Standard equipment of the SX3 GNSS software receiver is a precise temperature-controlled oscillator.)
The proofed difference correlator notably ruggedizes acquisition and tracking of any navigation satellite signals. Polyfit tracking reduces measurement noise through averaging (such as code/carrier measurements). (See “Innovation: Software GNSS Receiver.”) Accordingly, vector tracking improves the tracking of weak signals in degraded environments and the reacquisition of “lost” satellites.
Just like its predecessor, it is also able to act as a framework for a customer’s own signal processing algorithms. “Customers can fully concentrate on their applications instead of dealing with potentially obscure code when using open source,” said Product Manager Bernhard Riedl. “Our professional support is specifically dedicated to scientific work as well as SX3’s capability for additional customizations. SX3 is far more than just a COTS product. This makes IFEN’s new SX3 scientific software receiver a mighty powerful tool for research and development.”
Carolyn P. McDonald, president and CEO of NavtechGPS, catches up with GPS World at the ION GNSS+ Conference September 9-12, 2014, at the Tampa Convention Center in Tampa, Florida. Franck Boynton, vice president and CTO of NavtechGPS, also shares about simulators with software, the company’s OEM presence and more.
Spirent Federal Systems, a U.S. provider of positioning, navigation and timing test solutions to the government and its contractors, announces that its GSS9000 RF constellation simulator has been reviewed and granted security approval by the GPS Directorate.
Higher dynamic simulations with more accuracy and fidelity are enabled by 1000-Hz (1 ms) System Iteration Rate — a four-fold increase over Spirent’s current GSS8000 product — zero inter-channel bias and a 0.3 mm RMS pseudorange accuracy. The GSS9000 also includes support for restricted and classified signals from the GPS and Galileo systems as well as advanced capabilities for ultra-high dynamics.
According to Spirent, the GSS9000 is being rapidly adopted worldwide by key GNSS system and solution developers and providers because of its flexibility, performance and capability. The GSS9000 builds on the capability and performance of previous solutions from Spirent.
The GSS9000 is highly flexible and can support the widest range of carriers, ranging codes and data streams for the GPS, GLONASS, Galileo and BeiDou as well as regional/augmentation systems. Its flexibility is key to supporting tailored and customizable solutions for specific and unique test needs. Multi-antenna/multi-vehicle simulation, for differential-GNSS and attitude determination, and interference/jamming and spoofing testing are also supported.
Rockwell Collins has successfully received and tracked a Galileo satellite signal using a prototype GNSS receiver designed for secure military use.
In 2013, Rockwell Collins received a $2 million contract from the Air Force Research Laboratory (AFRL) and the GPS Directorate to develop and demonstrate a Secure Software Defined Radio (S-SDR) GNSS receiver capability. By using multiple available satellite signals, improved and more robust signal availability can be obtained, enabling a compatible GNSS receiver to deliver superior position determination that can improve navigation performance and signal availability.
Hosted in a software-defined radio, the S-SDR program will develop the security architecture required for receiver equipment approvals and certifications. The arrival of modernized GPS signals and other global constellations is changing the way the U.S. military and its allies accomplish secure GNSS-based positioning, navigation and timing. The European Galileo constellation coming on line during 2015, including its open signals and secure Public Regulated Service, is expected to provide an opportunity for improved robustness in satellite based navigation, in both commercial and government applications.
“This milestone reinforces our belief that Rockwell Collins is uniquely positioned to produce a navigation receiver that will meet global needs,” said John Borghese, vice president of the Advanced Technology Center for Rockwell Collins. “With decades of experience developing GPS systems and leading edge security architectures, our company continues to be a top innovator in this field.”
More than 35 years ago, Rockwell Collins assisted the U.S. Air Force in developing GPS technology. That legacy continued when the company created the world’s first all-digital miniature GPS receiver under contract with DARPA. Over the years, Rockwell Collins has produced more than 50 GPS products and delivered more than 1 million GPS receivers for commercial avionics and government applications. The GNSS receiver technology being provided for the S-SDR program will continue this legacy of providing leading edge GNSS solutions.
The announcement is notable in that it expands the relationship formed in June 2011 between Septentrio and Altus, with Septentrio more closely integrating the Altus subsidiary. Additionally, Septentrio is now manufacturing Altus GNSS RTK receivers at its factory in Belgium while Septentrio is closing its separate sales office in the U.S., merging that functionality into Altus, according a spokesman.
The Torrance, California-based Altus, started by Vancans in 2007, has long focused on the surveying sector. Vancans himself is a Fellow of the Royal Institution of Chartered Surveyors in the U.K.
“Ten years ago 90% of the high-precision GPS market was survey or survey-related. But survey is not a high-growth market. Today survey is probably 20% of the market and that’s doing things like putting a $10,000 receiver on a $4,000 lawn mower,” Vancans explains. “The growth market outside that (in consumer wireless) is huge, and it offers many new opportunities and will continue to grow.”
The survey market will continue to be in the Altus and Septentrio strategy, particularly leveraging Altus products with Septentrio’s advanced receiver technology experience in the OEM market.
Vancans has watched for two decades as use in emerging Asian economies has increased demand for surveying equipment and speculates what’s happening in the U.S. and other Western markets with OEM growth will eventually be mirrored there. He estimates the Chinese receiver market alone has grown ten-fold since he worked as president of Leica GPS nearly 20 years ago.
“What’s interesting and exciting is that it will be a big growth market for OEM or non-survey applications based on domestic Asian manufacturers using Western and increasingly Asian OEM,” Vancans says.
“If you can master the distribution capabilities in the OEM market in North America in the next couple of years, that will form the foundation of what happens in Asia in the future.”
Altus’ announcement also came with news the company hired Mo Kapila as OEM sales manager for Septentrio products. Kapila’s background is in embedded wireless, according to Vancans.
Vancans, who spent two days on the CTIA show floor, says the consumer wireless industry is on Altus’ radar, although he is still “very wary” of that side of the business. As general manager of Thales Navigation (which later re-merged as Magellan) in the early-2000s the company worked on a GPS attachment for Palm and integration into other consumer devices.
“The professional high-precision market is stable and products have a long shelf life,” he says. “On the other hand, the good thing with the consumer market is the constant changes in devices, the churning. As consumer markets take up high-precision GNSS products , they will be embedded in products which are rapidly outdated.”
Altus is taking a wait-and-see approach when it comes to professional-grade receivers for the consumer market.
“If the price lowers, the longevity will too,” he says. “The high end will likely go down to meet the low end – the cheap and easy, changeable model.”
Vancans says Septentrio will continue to differentiate itself from competitors based on its low power consumption relative to the functionality and size of the device, and robust positioning, whether it’s for professionals or consumers.
“If you look to the future and think of how much satellites will proliferate and signal availability will open,” he says, “it’s a good position for us to be in with the continuous consumerization of high-precision GPS and GNSS.”
Experts representing the Galileo Program provided a frank and open update on how it is addressing the problem of the first two full operational capability (FOC) satellites being delivered to the wrong orbit. The presentation was part of the panel discussion “Status of GPS, GLONASS, Galileo, BeiDou, and QZSS” at ION GNSS+ Wednesday morning.
No one from Rocosmos attended to present information on the status of GLONASS. A Russian spokesperson had hoped to come but could not obtain a visa, for unknown reasons. There appear to be no Russians at the conference apart from one CEO of a Russian receiver manufacturing firm.
A new article in Nature magazine provides additional background on the Galileo FOC anomaly. Also, the CANSPACE listserv has been engaged in discussing the issue.
An inquiry board is looking into problem to find the root cause of the anomaly. The board has already met several times.
An intermediate report is due shortly; a final report and recommendations will come next month.
The European Space Agency (ESA) is considering what can be done with the two satellites; ESA hopes to be able to use them operationally as much as possible.
ESA is also looking at the impact on the commercial Galileo service and the search-and-rescue service.
ESA is already narrowing down the possible causes of the anomaly.
ESA is waiting for the enquiry board to report before deciding on when and how the next two satellites will be launched.
The payloads of the errant satellites are currently off.
ESA wants to try to raise the perigees of the satellites to get them out of the van Allan radiation belt as soon as possible to prevent damage to the satellites. Raising the perigrees will also to reduce the maximum Doppler frequency shift from 9.6 kHz to at least 6.8 kHz to allow receivers to easily acquire and track the satellites, but leave enough hydrazine for future station keeping.
ESA is looking at the almanac problem and whether unused bits in the Galileo navigation message might be able to support a special almanac for the satellites.
ESA is also looking at possible rephasing of the satellites to optimize their use with the other satellites in the constellation.
