GPS World

Tag: Research Roundup

  • AI helps create street maps from satellite imagery

    AI helps create street maps from satellite imagery

    Creating detailed street maps and keeping them updated is an expensive and time-consuming task performed mostly by large companies. They ignore the many parts of the world where this task is not profitable, even though the need is high due to rapid growth and change in the street network, such as in Thailand.

    To automate the process and make accurate digital maps available in any country, researchers at the Massachusetts Institute of Technology (MIT) and the Qatar Computing Research Institute have developed an artificial intelligence (AI) model called RoadTagger. It uses satellite imagery to tag road features in digital maps, such as lane counts, which are essential for reliable navigation.

    Satellite imagery companies are constantly expanding their coverage and increasing their refresh rate, so this source of mapping data is more readily available and up to date than the data collected on the ground, such as by Google’s fleet of mapping cars. However, satellite imagery often suffers from occlusion from trees, buildings, overpasses and other obstacles.

    RoadTagger gets around this problem by using a combination of neural network architectures to predict hidden features. Testing of the model with digital maps of 20 U.S. cities showed that it predicted the number of lanes with 77% accuracy and the road type with 93% accuracy.

    An AI model developed at MIT and Qatar Computing Research Institute that uses only satellite imagery to automatically tag road features in digital maps could improve GPS navigation, especially in countries with limited map data. (Image: Google Maps/MIT News)
    An AI model developed at MIT and Qatar Computing Research Institute that uses only satellite imagery to automatically tag road features in digital maps could improve GPS navigation, especially in countries with limited map data. (Map data: Google/MIT News)

    RoadTagger, which combines a convolutional neural network (CNN) and a graph neural network (GNN) is fed only raw data and automatically produces output, without human intervention. The CNN, commonly used for image-processing tasks, takes as input raw satellite images of target roads. The GNN — widely used to model relationships between connected nodes in a graph — breaks the road into roughly 20-meter “tiles,” each of which is a separate graph node.

    For each node, the CNN extracts road features and shares that information with its immediate neighbors, thereby propagating road information along the whole graph. For example, if only two lanes of a four-lane road are visible in an image, the model uses information from nearby tiles, such as road width, to conclude that the road has four lanes.

    The researchers trained and tested RoadTagger using the OpenStreetMap data set. First, they collected confirmed road attributes from 688 square kilometers of maps of 20 U.S. cities, then they gathered the corresponding satellite images from a Google Maps dataset. The training taught the model what weight to assign to various features and node connections, and it now automatically learns which image features are useful and how to propagate those features along the graph.

    The researchers hope that RoadTagger will help humans validate the constant stream of changes in OpenStreetMap and similar datasets as well as enrich them with details that they do not already contain, such as whether a road is paved.

    Citation. He, S., Bastani, F., Jagwani, S., Park, E., Abbar, S., Alizadeh, M., Balakrishnan, H., Chawla, S., Madden, S., & Sadeghi, M. A. (Dec. 28, 2019). “RoadTagger: Robust Road Attribute Inference with Graph Neural Networks.” arXiv:1912.12408v1.

  • Research Roundup: Focus on maritime

    Research Roundup: Focus on maritime

    The 18,000-container-capacity CMA CGM Kuergelen. (Photo: CMA CGM)
    The 18,000-container-capacity CMA CGM Kuergelen. (Photo: CMA CGM)

    Of the 273 papers researchers presented this year at the Institute of Navigation’s annual ION GNSS+ conference, which took place in Miami on Sept. 16–20, the following five focused on maritime issues. Papers are available at www.ion.org/publications/browse.cfm.

    Automating the Sharing of Ocean Weather Data

    The Automatic Identification System (AIS) — mandatory for large ships and used by many mid-sized ones — was designed to help avoid collisions, enable shore authorities to provide vessel traffic services, and allow coastal states to monitor their waters. It also may be used to transmit other information between AIS stations onboard and ashore.

    In the aftermath of the sinking of the container ship El Faro in 2015, the U.S. National Transportation Safety Board (NTSB) and U.S. Coast Guard found a contributing factor was lack of reliable weather forecasts. The NTSB then recommended to the National Oceanic and Atmospheric Administration (NOAA) that it determine whether AIS could be used to share weather data collected by ships, to supplement the Voluntary Observing Ship (VOS) program where ships voluntarily submit weather observations to NOAA. The paper describes a successful test of this concept.

    Citation. Gregory Johnson, Ken Dykstra, Gaurav Dhungana and Brian Tetreault, “Sharing Ships’ Weather Data via AIS.”

    EGNOS for Maritime Navigation

    The European Geostationary Navigation Overlay System (EGNOS), which has been providing guidance to civil aviation since 2011, also can support maritime, railway and road applications. This paper assesses its use for maritime navigation compliant with International Maritime Organization (IMO) requirements for harbor entrances, harbor approaches and coastal waters: 99.8% of signal availability, 99.8% of service availability, 99.97% of service continuity, and 10 meters of horizontal accuracy. A kinematic test campaign was conducted in the waters of the Canary Islands using a geodetic multi-frequency, multi-constellation receiver-antenna pair installed aboard two vessels. The EGNOS Maritime Service met all IMO requirements by achieving a signal availability of 99.999%, a service availability in 99.9% of a predefined rectangular region, and 1.06 meters of horizontal accuracy at the 95th percentile. The service continuity requirement, however, was met in only 62.50% of the predefined region. Therefore, the paper concludes that the continuity risk is the most limiting factor for expanding the EGNOS Maritime Service along the coastal waters of the Canary Islands.

    Citation. Deimos Ibáñez Segura, Adria Rovira Garcia, Jaume Sanz, José Miguel Juan, Guillermo González Casado, María Teresa Alonso, José A. López Salcedo, Huamin Jia, Francisco Javier Pancorbo Garcia, Carlos Garcia Daroca, Irene Martin Calle, Santos Rodrigo Abadía Heredia and Manuel López Martínez, “A Kinematic Campaign to Evaluate EGNOS 1046 Maritime Service.”

    Options for Integrity

    Many maritime authorities are considering how to maintain the integrity of navigation systems as their infrastructure ages, especially given that the need for integrity in the user position is expected to increase with e-navigation services and for autonomous vessels. In harbor entrances, harbor approaches and coastal waters, the International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA) prescribes an absolute horizontal accuracy of ≤10 meters 95% of the time, with an integrity risk of 99.99999%. Today’s GNSS more than meets that accuracy requirement, so the driver is integrity. Options for integrity are marine radiobeacon DGPS/DGNSS, the primary augmentation system in use today; receiver autonomous integrity monitoring (RAIM); satellite-based augmentation systems (SBAS); and others (such as commercial services or inertial.). The European MarRINav project is investigating resilient PNT options to support UK Critical National Infrastructure. Part of this work is comparing EGNOS and marine radiobeacon DGPS performance to inform international discussions and receiver standardization.

    Citation. Alan Grant, George Shaw and Martin Bransby, “Considering SBAS and marine radiobeacon corrections to support safe maritime operations.”

    Evaluation of WAAS for Use in Canadian Waters

    Mariners navigating in Canadian waters use a ground-based augmentation system (GBAS) that provides differential corrections and integrity monitoring of GPS. This GBAS has been provided since 1994 by the Canadian Coast Guard (CCG) in the form of a differential GPS (DGPS) broadcast service. The service is only provided south of latitude 60°N in collaboration with the U.S. Coast Guard. Before embarking on a recapitalization program of its 24-year-old DGPS, and given that the U.S. Coast Guard is progressively shutting down its National Differential GPS sites, the CCG is evaluating options for its own DGPS network. Options include the wide-area augmentation system (WAAS), originally developed by the U.S. Federal Aviation Administration for civil aviation. This paper describes the authors’ evaluation for the CCG to determine the expected accuracy, integrity and availability of WAAS throughout Canadian waters, concluding that the current WAAS provides acceptable accuracy and integrity for most of Canada, excluding the higher latitudes.

    Citation. Gregory Johnson, Gaurav Dhungana and Jean Delisle, “An Evaluation of WAAS 2020+ to Meet Maritime Navigation Requirements in Canadian Waters.”

    GNSS + INS for Attitude Determination

    Attitude determination (AD) is an important navigation component for ships and spacecraft. GNSS enables resolving their orientation in a precise and absolute manner, by employing multiple antennas rigidly mounted on the vessel. This requires carrier-phase observations, with the consequent added complexity of resolving integer ambiguities. Inertial aiding has been extensively exploited for AD, because it enables tracking fast rotation variations and bridging short periods of GNSS outage. In this paper, the fusion of inertial and GNSS information is exploited within the recursive Bayesian estimation framework, applying an Error State Kalman Filter, which, unlike common Kalman filters, tracks the error or variations in the state estimate, posing meaningful advantages for AD. The results show that the inertial aiding, along with a constrained attitude model for the float estimation, significantly improve the performance of attitude determination compared to classical unaided baseline tracking.

    Citation. Daniel Medina, Vincenzo Centrone, Ralf Ziebold, and Jesús García, “Attitude Determination via GNSS Carrier Phase and Inertial Aiding.”

  • Research Roundup: Soft information for IoT positioning

    Soft information for IoT positioning

    The billions of interconnected devices and sensors embedded in other devices, vehicles and even humans that collectively constitute the much-heralded internet of things (IoT) collect and share data used in myriad applications. This requires them to know their location, which is a challenge in GPS-denied environments, such as most indoor locations, tunnels and urban canyons.

    A new approach helps networks of smart devices cooperate to find and communicate their positions in such environments. This “localization of things” could be helpful in applications ranging from autonomous vehicles to asset tracking, from supply-chain monitoring to smart cities and real-time mapping.

    Traditional network localization methods estimate a single value for each geospatial variable, such as the distance between two nodes. Therefore, accuracy drops sharply in environments where multipath, a limited view of the sky, and other problems severely degrade GNSS and wireless signals. A paper by researchers at four institutions outlines a system to capture location information even in these challenging environments by fusing positional data of various kinds as well as digital maps.

    The new method fuses data from various sensing measurements — such as radio, optical and inertial signals — and analyzes features of each signal — including its power, angle of arrival, and time of flight. It uses machine-learning techniques to weigh this “soft information” — the researchers call it that because their method does not favor any single “hard” number — to create a probability distribution of distances, angles and other metrics.

    It also exploits contextual information from digital maps, dynamic models and node profiles to verify what is possible. For example, two nodes could not be 20 meters apart if they are both in an area with a maximum dimension of 10 meters.

    To reduce the complexity and size of the data that it must collect to function, the new method identifies the most and least useful aspects of the received waveforms for the purpose at hand on the basis of a “principal component analysis.”

    In simulations of challenging scenarios, full of reflections and echoes, the new system’s performance significantly surpassed that of traditional ones and consistently approached the theoretical limit for localization accuracy, while the accuracy of traditional systems dropped dramatically.

    Citation: “Soft Information for Localization-of-Things” by A. Conti, S. Mazuelas, S. Bartoletti, W. Lindsey and M. Win, Sept. 9, 2019, Proceedings of the IEEE.

    Algorithm helps civil aircraft fight spoofing

    Evolution in civil aviation foresees a greater role for GNSS in onboard navigation systems as opposed to traditional terrestrial navigation aids. This will require improvements in managing the threat posed by RF interference.

    Fortunately, the availability of more GNSS constellations and two carrier frequencies now enables GNSS equipment used aboard civil aircraft to not only detect and monitor spoofing, but also determine from which direction it is coming.

    A recent paper details a procedure to do this. It consists of a detection module that employs an algorithm to identify which signals tracked by the receiver are counterfeit, if any, followed by a direction-finding module that implements an efficient direction-of-arrival (DOA) estimator. The procedure requires three GNSS antennas and the same number of receivers, time-synchronized with a common clock, plus a signal processor that implements the detection and DOA estimation algorithms. The paper presents the design of the chain of algorithms and their preliminary tests in a laboratory setup, with the simulation of several spoofing attacks, assumed realistic in a civil aviation scenario.

    Citation:  “An Algorithm for Finding the Direction of Arrival of Counterfeit GNSS Signals on a Civil Aircraft” by G. Falco, M. Nicola, E. Falletti and M. Pini, presented on Sept. 20, 2019, at the ION GNSS+ conference in Miami, Florida.

    Joint Galileo/GPS Project on the ISS

    The European Space Agency (ESA) and NASA conducted a joint Galileo/GPS space receiver experiment aboard the International Space Station (ISS). The objectives of the project were to demonstrate the robustness of a combined Galileo/GPS waveform uploaded to NASA hardware already operating in the challenging space environment — the SCaN (Space Communications and Navigation) software defined radio (SDR) testbed (FPGA) — on-board the ISS.

    The activities included the analysis of the Galileo/GPS signal and on-board position/velocity/time (PVT) performance, processing of the Galileo/GPS raw data (code and carrier phase) for precise orbit determination, and validation of the added value of a space-borne dual GNSS receiver compared to a single-system GNSS receiver operating under the same conditions. A recent paper provides a general overview of the experiment (called GARISS) and describes its design, test, validation, and operations. It also presents the various analyses conducted in the context of this project and the results obtained, with a focus on the (Precise) Orbit Determination results.

    Citation: “The joint ESA/NASA Galileo/GPS Receiver onboard the ISS – The GARISS Project” by W. Enderle, E. Schönemann, F. Gini, M. Otten, P. Giordano, J. Miller, S. Sands, D. Chelmins, O. Pozzobon, presented on September 20, 2019, at the ION GNSS+ conference in Miami, FL.

  • Early earthquake warnings: GNSS could enable 10-second alerts

    Early earthquake warnings: GNSS could enable 10-second alerts

    Previous research suggests that not until halfway through a rupture (90 seconds for a magnitude-9 quake) can magnitude be predicted. Geodetic GNSS data could bring this down to as little as 10 seconds — greatly extending and enhancing earthquake early warning systems.

    How soon can we predict the magnitude of an earthquake?

    Seismologists Diego Melgar of the University of Oregon and Gavin Hayes of the U.S. Geological Survey (USGS) in Golden, Colorado, tackled this question by chance while Melgar was writing code to simulate earthquakes to check the accuracy of Earthquake Early Warning systems in the Pacific Northwest.

    He reached out to Hayes, who curates a database for the USGS that contains “source time functions,” which show how the seismic energy release changes through time as the earthquake ruptures.

    As a rupture grows, the speed of growth changes, and source time function captures that change. Melgar and Hayes focused on the acceleration of the energy release in large (M>7) and great (M≥9) earthquakes, and found that acceleration wobbled between 2 and 5 seconds after the quakes began.

    In February 2016, the USGS rolled out the second-generation ShakeAlert Earthquake Early Warning test system in California. The diagram shows how the system would operate. (Image: USGS)
    In February 2016, the USGS rolled out the second-generation ShakeAlert Earthquake Early Warning test system in California. The diagram shows how the system would operate. (Image: USGS)

    However, with the approximately 250 M≥7 earthquakes in their database, they found that between 10 and 15 seconds after rupture began, these larger earthquakes started to behave similarly, and that behavior scales with their final magnitude, Hayes said. “In other words, the acceleration at 10 to 15 seconds is diagnostic of their final magnitude.”

    Earthquake ruptures sputter along for about 10 seconds, after which the big ones accelerate, according to Melgar and Hayes. Three different source time function databases showed the same consistency.

    Vertical movement near the source of large earthquakes can be between 3 and 5 meters, according to data from GNSS geodetic receivers. Analysis of near-source GNSS data from 12 M≥7 earthquakes showed that for the first 10 seconds after the first indication of an earthquake was recorded, the earthquakes made almost immeasurable movements. But between 10 and 15 seconds, the amount of vertical displacement began to rapidly diverge for the different magnitude groupings. By 20 to 25 seconds, the vertical movement was distinct.

    Previous research indicated roughly half the source duration must pass before an accurate prediction could be made. Cutting the prediction time down to 15 seconds would be invaluable to earthquake early warning systems and tsunami prediction algorithms, where every second counts.

    GNSS sensors are installed onshore across the globe, but the majority of megathrust earthquakes occur underwater. To integrate Melgar and Hayes’ findings effectively into earthquake early warning systems would require sensors installed along the seafloor, they noted. “You [would also] need to have fiber-optic cables from shore to the bottom of the ocean, winding around with sensors, and then eventually coming back on shore, and that’s not cheap,” Melgar said.

    An additional 10 to 30 seconds of warning to a city or nuclear reactor of an imminent quake would have enormous benefits. But if the hypothesis is wrong, using it now would lead to a greater rate of false alarms and missed quakes, eroding the value of these warnings to society. Melgar and Hayes acknowledged their finding needs to be rigorously tested.

    Summarized from Temblor’s website. The Temblor Android app and website provide earthquake, landslide, tsunami and flood information.

    Citation
    Tripathy-Lang A. (2019), “Can the size of a large earthquake be foretold just 10 seconds after it starts?”. Temblor, http://doi.org/10.32858/temblor.029

  • Research Roundup: Autonomous aircraft landings

    Image-based positioning has not yet been certified in aviation applications. To cover numerous environmental conditions, the authors installed various optical sensors. They present an approach for fusing image data of two complementary cameras with different spectral ranges.

    The use of two image sensors working in the visible light spectrum and infrared spectrum increases availability and accuracy, meeting requirements to be used as an augmentation for state-of-the art GNSS-based landing systems.

    This investigation presents real flight data processed by means of the proposed method. This work constitutes a new approach for robust runway detection, since position calculation was only carried out once in one time epoch on a single blended image.

    The proposed method was applied to data from two flight campaigns in post-process. A determined set of parameters lead to a sufficient level of availability and a valid runway detection throughout the final approach.

    Citation
    M. Angermann, S. Wolkow, A. Dekiert, U. Bestmann, P. Hecker (2018), “Linear blend: Data fusion in the image domain for image-based aircraft positioning during landing.” Pacific PNT Conference, www.ion.org/publications/browse.cfm

    Aircraft navigation during landing approach is mostly supported by ground-based landing systems in commercial aviation, which cause high installation and maintenance costs.

    Nevertheless, the final sequence of the flight before touchdown is mostly performed by the pilot manually, because of the high requirements for accuracy and integrity. Only a few landing systems can fulfill these requirements during the last 200 feet above ground.

    The current work presents a further development of an optical positioning system to be deployed below 200 feet and on ground after touchdown in order to be used as an additional source for positioning information. The system is capable of visual 3D positioning of the aircraft relative to the runway.

    Algorithms for threshold marking (see image below) and centerline detection, as well as lateral position calculation during rollout are presented. The system is evaluated during flight trials performed with the research aircraft Dornier Do 128-6.

    Citation
    S. Wolkow, M. Angermann, A. Dekiert, U. Bestmann (2018), “Model-based threshold and centerline detection for aircraft positioning during landing approach.” Pacific PNT Conference, www.ion.org/publications/browse.cfm

  • Research Roundup: Using UAVs as GNSS satellites

    Real-time real-world testbed for new signals

    By Daniel S. Maier, Thomas Kraus, Daniela E. Sánchez, Ronny Blum and Thomas Pany, Universität der Bundeswehr München

    This research paper presents an update of the authors’ real-time real-world testbed for new GNSS signals. It includes experience gained in setting up an airborne pseudolite, UAVlite, to analyze the code- and phase-ranging performance and to test navigation message authentication schemes.

    UAVlites transmit GNSS-like signals free from any local transmitter multipath, in contrast to ground-based transmitters. A software-defined radio allows easy broadcast of new navigation signals, which can be tested in real environments.

    Purpose. To improve GNSS signals, it is important to test and analyze signal performance under various conditions and harsh environments. This is done mainly with computer simulation. However, a simulation always relies on assumptions and simplifications of a real-world problem.

    Therefore, the authors are developing a flexible, cost-efficient and highly adjustable test system, usable for real test scenarios. With this system, researchers can investigate GNSS signal structures, range performance, authentication methods, channel coding and signal behavior under foliage, blockage, jamming, spoofing and other interferences.

    Testbed Setup. Key elements include a UAVlite, two ground stations and a composite binary offset carrier signal. The system has demonstrated decimeter code-range accuracy and millimeter phase-range accuracy. Performance of a Galileo Open Service Navigation Message Authentication implementation was also analyzed.

    The testbed has potential in the field of signal analysis and optimization, especially in multipath, channel coding, authentication or robustness against jamming, spoofing or other interference for existing GNSS signals, and for developing potential new GNSS signals.

    This paper was presented at ION-GNSS+ 2018. See www.ion.org/publications/browse.cfm.

  • Using consumer-grade sensors for precise positioning

    By Urs Niesen, Jubin Jose, Xinzhou Wu, Qualcomm Technologies Inc.

    Emerging automotive applications require reliable but at the same time low-cost positioning solutions. In this paper, we present such a solution by fusing the measurements from several consumer-grade sensors using a tightly coupled centralized filter.

    The sensors used are a single-frequency GNSS receiver providing GPS and GLONASS pseudoranges and GPS carrier-phase measurements, a micro-electro-mechanical (MEMS) inertial measurement unit (IMU), a monocular camera, wheel-speed and steering-angle sensors.

    We also employ vehicular constraints, integrated as pseudo-measurements. The centralized fusion architecture allows sensor cross-calibration and improves outlier detection. The filter runs in real time on the target platform, producing pose estimates at 30 Hz. Through extensive experimental evaluations, we demonstrate positioning accuracies of sub-meter 95-percentile horizontal errors even in GNSS-challenged deep-urban scenarios.

    Conflicting Requirements. Accurate positioning is a requirement for several emerging vehicular applications such as advanced driver-assistance systems (ADAS) and autonomous driving. Positioning solutions for these applications face two competing constraints. To be technically viable, the computed position estimate needs to be reliable in scenarios ranging from open sky to deep urban, with less than 1-meter 95-percentile horizontal error as an often-mentioned target. To be economically viable, the system needs to be built from consumer-grade components.

    We reconcile these conflicting requirements by fusing measurements from several low-cost sensors into a single pose estimate using one centralized extended Kalman filter (EKF). A multi-constellation single-frequency GNSS receiver provides GPS pseudorange and carrier-phase measurements and GLONASS pseudorange measurements. These are combined in a tightly coupled integration architecture with a consumer-grade MEMS IMU used to produce the reference navigation solution.

    Tight integration enables outlier rejection directly for the raw GNSS measurements. This is crucial in deep-urban scenarios, since many or most raw GNSS measurements could be outliers in these conditions. We use a monocular camera and vehicular sensors, providing four wheel-speed measurements and a steering-angle measurement, as additional aiding sensors.

    Constraints. Finally, vehicular constraints are integrated as pseudo-measurements. These sensors have very different noise sources and failure modes, which allows cross-calibration and improves failure and outlier detection. Given the tightly coupled integration in a single EKF, the filter state is quite large and can reach more than 100 dimensions. Despite its size, we are able to run the filter in real time and on target, producing pose outputs at a rate of 30 Hz.

    We report the result of extensive experimental evaluations in different scenarios ranging from open sky with good satellite visibility to deep urban with long stretches of no or only limited satellite visibility. In each of these scenarios, we obtain the target accuracy of sub-meter 95% horizontal positioning error.

    We show that, in the benign open-sky scenarios, GPS and IMU sensors are sufficient to achieve the target accuracy. However, in challenging deep-urban scenarios, all the integrated sensors are required to attain reliable sub-meter positioning performance.

    Sensors and Components. We use Qualcomm SiRFstarV 5e B02 GNSS chipset, a low-cost commercial GNSS product, connected to a NovAtel GPS-702-GG dual-frequency GPS+GLONASS Pinwheel antenna, the only component not consumer-grade, to separate impact of a specific antenna on performance. We plan to evaluate low-cost antennas in the future. We use a TDK InvenSense low-cost MEMS 6-axis IMU (MPU-6150) and a vehicle interface with vehicle sensors through the controller area network bus. Accurate timestamping for tightly coupling sensor measurements is provided by a custom sensor sync board. The processor is a Qualcomm Snapdragon 820 automotive platform for real-time computation. (Qualcomm SiRFstar and Qualcomm Snapdragon are products of Qualcomm Technologies, Inc. and/or its subsidiaries.)

    This paper was presented at ION-GNSS+ 2018.
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  • Research Roundup: Design and evaluation of integrity algorithms for PPP in kinematic applications

    By Kazuma Gunning, Juan Blanch and Todd Walter, Stanford University, and Lance de Groot and Laura Norman, Hexagon Positioning Intelligence

    UAV and autonomous platforms can greatly benefit from an assured position solution with high integrity error bounds. The expected high degree of connectivity in these vehicles will allow users to receive real-time precise clock and ephemeris corrections, which enable the use of precise point positioning (PPP) techniques.

    Until now, these techniques have mostly been used to provide high accuracy, rather than focusing on high-integrity applications. The authors apply the methodology and algorithms used in aviation to determine position error bounds with high integrity (or protection levels) for a PPP position solution.

    PPP techniques can provide centimeter accuracy without local reference stations in kinematic applications. These techniques have so far mostly been used to provide high accuracy, and it is only recently that they have been proposed to provide integrity, that is, position error bounds with a very low probability of exceeding them.

    There has been preliminary work on the application of integrity to PPP, but it remains a challenge to translate the benefits of PPP to accuracy while maintaining high integrity. Most of the integrity work in PPP and real-time kinematic (RTK) has dealt more with the ambiguity resolution process under nominal error conditions and less on the integrity of the position solution under fault conditions.

    The authors overview their PPP filter implementation, and describe the threat model as well as two classes of integrity algorithms: solution separation and sum of squared residuals based (also called residual-based [RB], a misnomer, as all autonomous integrity monitors are based on the residuals.)

    They present data sets used to evaluate the algorithms, compare the protection levels (PLs) obtained with different algorithms, and present the results obtained with the most promising PL formulation in four different data sets: static, dynamic in open-sky conditions, dynamic in midtown suburban conditions, and in flight.

    Concluding, they state: “We have formulated RAIM protection-level formulas using either solution separation or the sum of residual squares. Both formulations consist of straightforward adaptations of snapshot RAIM to a Kalman filter solution.

    “For solution separation, we have shown an implementation where the computational cost of running a bank of filters is far from being proportional to the cost of one filter. Instead, we could run 50 additional filters for the cost of one.

    “For residual based RAIM we have developed a set of formulas to update the sum of square residuals from one time step to the next one. Because this test statistic is exactly the same as the one used in snapshot RAIM (when we consider the problem as a batch least squares), we could use the formula that ties the slope of a fault mode to the standard deviation of the solution separation. The slope can therefore also be updated recursively.”

    Finally, “we have refined the PPP filter, added one scenario (suburban driving conditions), and examined the effect of considering multiple faults in the formulation of the test statistics and the protection levels. The results are very promising: protection levels below 2 m appear to be achievable, and the computation load is lower than expected.”

    This paper was presented at ION-GNSS+ 2018. See www.ion.org/publications/ browse.cfm.

  • Research Roundup: Mapping reveals Easter Island secrets

    Research Roundup: Mapping reveals Easter Island secrets

    Point-process mapping links Easter Island statuary to freshwater sources.

    By Robert DiNapoli, University of Oregon; Carl Lipo, Binghampton University; Tanya Brosnan and Matthew Becker, California State University, Long Beach; Terry Hunt, University of Arizona; Sean Hixon, Pennsylvania State University; Alex E. Morrison, University of Auckland.

    Rapa Nui (Easter Island, Chile) is famous for its elaborate ritual architecture: more than 300 monumental platforms (ahu) and nearly 1,000 monumental, multi-ton anthropomorphic statues (moai). To date, however, we lack explicit modeling to explain spatial and temporal aspects of monument construction.

    Photo: Steven Sullivan/Shutterstock.com
    Photo: Steven Sullivan/Shutterstock.com

    In a span of only about 500 years, from the 13th century A.D. to European contact in A.D. 1722 and into historic times, the Rapanui islanders sculpted and erected these famous megalithic statues.

    Why? And why were they placed where they stand?

    For many years, scholars thought that the island must have supported a larger and more complex society under more prosperous environmental conditions that then collapsed following a self-imposed “ecocide.” In recent years, nearly every major component of this narrative has been shown to lack empirical sufficiency.

    In this paper, we use spatially explicit point-process modeling to explore the potential relations between ahu construction locations and subsistence resources, namely rock-mulch agricultural gardens, marine resources and freshwater sources — the three most critical resources on Rapa Nui.

    Through these analyses, we demonstrate the central importance of coastal freshwater seeps. Our results suggest that ahu locations are most parsimoniously explained by distance from freshwater sources, in particular coastal seeps, with important implications for community formation and inter-community competition in precontact times.

    The island’s marginal ecology limited the food options available to the inhabitants. These environmental constraints could be a key factor in the emergence of monuments on Rapa Nui, such as their role as adaptive responses to environmental uncertainty or as territorial signals of control over limited resources.

    We quantitatively modeled how the spatial distribution of ahu is explained by different resources thought to be the focus of competition.

    Point-process models (PPM) are a wide class of spatially explicit models that facilitate formal analysis of the relationship between point-patterns and a range of spatial covariates. PPM works by fitting a spatial intensity function to the intensity of an empirical point pattern and finding the values of the predictor variables (i.e., parameters) that best fit the data.

    The technique is similar to geographically weighted regression or maximum entropy modeling but has a number of strengths, such as its ability to simultaneously model both first-order and second-order properties in the underlying point-pattern and how these properties may be dependent upon a set of underlying spatial covariates.

    Our full paper in the January 2019 issue of PLOS One, an open-access scientific journal published by the Public Library of Science, presents a series of formal models that indicate that if Rapa Nui’s monuments did indeed serve a territorial display function (in addition to their well-known ritual roles), then their patterns are best explained by the availability of the island’s limited freshwater.

    More info at https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210409.