Cooperative Interference Detection, Localization, and Mitigation in GNSS

Due to the low cost of GNSS receivers and their consequent diffusion, a wide range of location-aware applications are arising. Some of these applications are critical and have strict requirements in terms of availability, integrity and reliability. Examples of critical applications are precision landing and en-route navigation in air transportations; automated highways and mileage-based toll in road transportations; search and rescue in safety of life applications. A failure in fulfilling one or more requirements of a critical application may have dramatic consequences and cause serious damage. One of the most challenging threats for critical GNSS application, is represented by interference. In particular, jamming devices, operating inside GNSS bands, are easily and cheaply purchasable on the Internet. These devices transmit disturbing signals with the aim of preventing the correct operations of GNSS receivers. In order to satisfy the requirements of critical applications, it is necessary to promptly detect, localize and remove such interfering sources. Moreover, it is important to characterize the interfering signals in order to develop interference avoidance and mitigation techniques that ensure robustness of GNSS receivers to interference. This thesis studies the problem of interference in GNSS, from a cooperative perspective

Interference Mitigation and Localization Based on Time-Frequency Analysis for Navigation Satellite Systems

Interference Mitigation and Localization Based on Time-Frequency Analysis for Navigation Satellite SystemsNowadays, the operation of global navigation satellite systems (GNSS) is imperative across a multitude of applications worldwide. The increasing reliance on accurate positioning and timing information has made more serious than ever the consequences of possible service outages in the satellite navigation systems. Among others, interference is regarded as the primary threat to their operation. Due the recent proliferation of portable interferers, notably jammers, it has now become common for GNSS receivers to endure simultaneous attacks from multiple sources of interference, which are likely spatially distributed and transmit different modulations. To the best knowledge of the author, the present dissertation is the first publication to investigate the use of the S-transform (ST) to devise countermeasures to interference. The original contributions in this context are mainly: • the formulation of a complexity-scalable ST implementable in real time as a bank of filters; • a method for characterizing and localizing multiple in-car jammers through interference snapshots that are collected by separate receivers and analysed with a clever use of the ST; • a preliminary assessment of novel methods for mitigating generic interference at the receiver end by means the ST and more computationally efficient variants of the transform. Besides GNSSs, the countermeasures to interference proposed are equivalently applicable to protect any direct-sequence spread spectrum (DS-SS) communication

Distributed localization of a RF target in NLOS environments

We propose a novel distributed expectation maximization (EM) method for non-cooperative RF device localization using a wireless sensor network. We consider the scenario where few or no sensors receive line-of-sight signals from the target. In the case of non-line-of-sight signals, the signal path consists of a single reflection between the transmitter and receiver. Each sensor is able to measure the time difference of arrival of the target's signal with respect to a reference sensor, as well as the angle of arrival of the target's signal. We derive a distributed EM algorithm where each node makes use of its local information to compute summary statistics, and then shares these statistics with its neighbors to improve its estimate of the target localization. Since all the measurements need not be centralized at a single location, the spectrum usage can be significantly reduced. The distributed algorithm also allows for increased robustness of the sensor network in the case of node failures. We show that our distributed algorithm converges, and simulation results suggest that our method achieves an accuracy close to the centralized EM algorithm. We apply the distributed EM algorithm to a set of experimental measurements with a network of four nodes, which confirm that the algorithm is able to localize a RF target in a realistic non-line-of-sight scenario.Comment: 30 pages, 11 figure

Multi-Satellite Orbit Determination Using Interferometric Observables with RF Localization Applications

Very long baseline interferometry (VLBI) specifically same-beam interferometry (SBI), and dual-satellite geolocation are two fields of research not previously connected. This is due to the different application of each field, SBI is used for relative interplanetary navigation of two satellites while dual-satellite geolocation is used to locate the source of a radio frequency (RF) signal. In this dissertation however, we leverage both fields to create a novel method for multi-satellite orbit determination (OD) using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements. The measurements are double differenced between the satellites and the stations, in so doing, many of the common errors are canceled which can significantly improve measurement precision. Provided with this novel OD technique, the observability is first analyzed to determine the benefits and limitations of this method. In all but a few scenarios the measurements successfully reduce the covariance when examining the Cramér-Rao Lower Bound (CRLB). Reduced observability is encountered with geostationary satellites as their motion with respect to the stations is limited, especially when only one baseline is used. However, when using satellite pairs with greater relative motion with respect to the stations, even satellites that are close to, but not exactly in a geostationary orbit can be estimated accurately. We find that in a strong majority of cases the OD technique provides lower uncertainties and solutions far more accurate than using conventional OD observables such as range and range-rate while also not being affected by common errors and biases. We specifically examine GEO-GEO, GEO-MEO, and GEO-LEO dual-satellite estimation cases. The work is further extended by developing a relative navigation scenario where the chief satellite is assumed to have perfect knowledge, or some small amount of uncertainty considered but not estimated, while estimating the deputy satellite state with respect to the chief. Once again the results demonstrate that the TDOA and FDOA OD results are favorable with faster dynamics over classical measurements. This dissertation not only explores the OD side, but also gaps in geolocation research. First the mapping of ephemeris uncertainty to the geolocation covariance to provide a more realistic covariance was implemented. Furthermore, the geolocation solution was improved by appending a probabilistic altitude constraint to the posterior covariance, significantly reducing the projected geolocation uncertainty ellipse. The feasibility of using the geolocation setup to passively locate a LEO satellite was also considered. Finally the simulated results were verified using a long-arc of real data. The use of FDOA for small-body navigation and gravity recovery was also examined as an extended application

Local Positioning Systems in (Game) Sports

Position data of players and athletes are widely used in sports performance analysis for measuring the amounts of physical activities as well as for tactical assessments in game sports. However, positioning sensing systems are applied in sports as tools to gain objective information of sports behavior rather than as components of intelligent spaces (IS). The paper outlines the idea of IS for the sports context with special focus to game sports and how intelligent sports feedback systems can benefit from IS. Henceforth, the most common location sensing techniques used in sports and their practical application are reviewed, as location is among the most important enabling techniques for IS. Furthermore, the article exemplifies the idea of IS in sports on two applications

EXPERIMENTAL EVALUATION OF MODIFIED PHASE TRANSFORM FOR SOUND SOURCE DETECTION

The detection of sound sources with microphone arrays can be enhanced through processing individual microphone signals prior to the delay and sum operation. One method in particular, the Phase Transform (PHAT) has demonstrated improvement in sound source location images, especially in reverberant and noisy environments. Recent work proposed a modification to the PHAT transform that allows varying degrees of spectral whitening through a single parameter, andamp;acirc;, which has shown positive improvement in target detection in simulation results. This work focuses on experimental evaluation of the modified SRP-PHAT algorithm. Performance results are computed from actual experimental setup of an 8-element perimeter array with a receiver operating characteristic (ROC) analysis for detecting sound sources. The results verified simulation results of PHAT- andamp;acirc; in improving target detection probabilities. The ROC analysis demonstrated the relationships between various target types (narrowband and broadband), room reverberation levels (high and low) and noise levels (different SNR) with respect to optimal andamp;acirc;. Results from experiment strongly agree with those of simulations on the effect of PHAT in significantly improving detection performance for narrowband and broadband signals especially at low SNR and in the presence of high levels of reverberation

Robust Localization for Mixed LOS/NLOS Environments With Anchor Uncertainties

Localization is particularly challenging when the environment has mixed line-of-sight (LOS) and non-LOS paths and even more challenging if the anchors’ positions are also uncertain. In the situations in which the parameters of the LOS-NLOS propagation error model and the channel states are unknown and uncertainties for the anchors exist, the likelihood function of a localizing node is computationally intractable. In this paper, assuming the knowledge of the prior distributions of the error model parameters and that of the channel states, we formulate the localization problem as the maximization problem of the posterior distribution of the localizing node. Then we apply variational distributions and importance sampling to approximate the true posterior distributions and estimate the target’s location using an asymptotic minimum mean-square-error (MMSE) estimator. Furthermore, we analyze the convergence and complexity of the proposed variational Bayesian localization (VBL) algorithm. Computer simulation results demonstrate that the proposed algorithm can approach the performance of the Bayesian Cramer-Rao bound (BCRB) and outperforms conventional algorithm

Acoustic underwater target tracking methods using autonomous vehicles

Marine ecological research related to the increasing importance which the fisheries sector has reached so far, new methods and tools to study the biological components of our oceans are needed. The capacity to measure different population and environmental parameters of marine species allows a greater knowledge of the human impact, improving exploitation strategies of these resources. For example, the displacement capacity and mobility patterns are crucial to obtain the required knowledge for a sustainable management of fisheries. However, underwater localisation is one of the main problems which must be addressed in subsea exploration, where no Global Positioning System (GPS) is available. In addition to the traditional underwater localisation systems, such as Long BaseLine (LBL) or Ultra-Short BaseLine (USBL), new methods have been developed to increase navigation performance, flexibility, and to reduce deployment costs. For example, the Range-Only and Single-Beacon (ROSB) is based on an autonomous vehicle which localises and tracks different underwater targets using slant range measurements conducted by acoustic modems. In a moving target tracking scenario, the ROSB target tracking method can be seen as a Hidden Markov Model (HMM) problem. Using Bayes' rule, the probability distribution function of the HMM states can be solved by using different filtering methods. Accordingly, this thesis presents different strategies to improve the ROSB localisation and tracking methods for static and moving targets. Determining the optimal parameters to minimize acoustic energy use and search time, and to maximize the localisation accuracy and precision, is therefore one of the discussed aspects of ROSB. Thus, we present and compare different methods under different scenarios, both evaluated in simulations and field tests. The main mathematical notation and performance of each algorithm are presented, where the best practice has been derived. From a methodology point of view, this work advances the understanding of accuracy that can be achieved by using ROSB target tracking methods with autonomous vehicles. Moreover, whereas most of the work conducted during the last years has been focused on target tracking using acoustic modems, here we also present a novel method called the Area-Only Target Tracking (AOTT). This method works with commercially available acoustic tags, thereby reducing the costs and complexity over other tracking systems. These tags do not have bidirectional communication capabilities, and therefore, the ROSB techniques are not applicable. However, this method can be used to track small targets such as jellyfish due to the reduced tag's size. The methodology behind the area-only technique is shown, and results from the first field tests conducted in Monterey Bay area, California, are also presented.La biologia marina junt amb la importància que ha adquirit el sector pesquer, fa que es requereixin noves eines per a l’estudi dels nostres oceans. La capacitat de mesurar diferents poblacions i paràmetres ambientals d’espècies marines permet millorar el coneixement de l’impacte que l’ésser humà té sobre elles, millorant-ne els mètodes d’explotació. Per exemple, la capacitat de desplaçament i els patrons de moviment són crucials per obtenir el coneixement necessari per a una explotació sostenible de les pescaries involucrades. No obstant, la localització submarina és un dels principals problemes que s’ha de resoldre en l’explotació dels recursos submarins, on el sistema de posició global (GPS) no es pot utilitzar. A part dels mètodes tradicionals de posicionament submarí, com per exemple el Long Base-Line (LBL) o el Ultra-Short Base-Line (USBL), nous mètodes han estat desenvolupats per tal de millorar la navegació, la flexibilitat, i per reduir els costos de desplegament. Per exemple, el Range-Only and Single-Beacon (ROSB) utilitza un vehicle autònom per a localitzar i seguir diferents objectius submarins mitjançant mesures de rang realitzades a partir de mòdems acústics. En un escenari on l’objectiu a seguir és mòbil, el mètode ROSB de seguiment pot ser vist com a un problema de Hidden Markov Model (HMM). Aleshores, utilitzant la regla de Bayes, la funció de distribució de probabilitat dels estats del HMM pot ser solucionat utilitzant diferents mètodes de filtratge. Per tant, s’estudien diferents estratègies per millorar el sistema de localització i seguiment basat en ROSB, tant per objectius estàtics com mòbils. En aquesta tesis, presentem i comparem diferents mètodes utilitzant diferents escenaris, els quals s’han avaluat tant en simulacions com en proves de camp reals. A més, es presenten les principals notacions matemàtiques de cada algoritme i les millors pràctiques a utilitzar. Per tant, des d’un punt de vista metodològic, aquest treball fa un pas endavant en el coneixement de l’exactitud que es pot assolir utilitzant els mètodes de localització i seguiment d’espècies mitjançant algoritmes ROSB i vehicles autònoms. A més a més, mentre molts dels treballs realitzant durant els últims anys es centren en l’ús de mòdems acústics per al seguiment d’objectius submarins, en aquesta tesis es presenta un innovador mètode anomenat Area-Only Target Tracking (AOTT). Aquest sistema utilitza petites etiquetes acústiques comercials (tag), la qual cosa, redueix el cost i la complexitat en comparació amb els altres mètodes. Addicionalment, gràcies a l’ús d’aquests tags de dimensions reduïdes, aquest sistema permet seguir espècies marines com les meduses. La metodologia utilitzada per el mètode AOTT es mostra en aquesta tesis, on també es presenten els primers experiments realitzats a la badia de Monterey a Califòrnia

Exploiting Sparse Structures in Source Localization and Tracking

This thesis deals with the modeling of structured signals under different sparsity constraints. Many phenomena exhibit an inherent structure that may be exploited when setting up models, examples include audio waves, radar, sonar, and image objects. These structures allow us to model, identify, and classify the processes, enabling parameter estimation for, e.g., identification, localisation, and tracking.In this work, such structures are exploited, with the goal to achieve efficient localisation and tracking of a structured source signal. Specifically, two scenarios are considered. In papers A and B, the aim is to find a sparse subset of a structured signal such that the signal parameters and source locations maybe estimated in an optimal way. For the sparse subset selection, a combinatorial optimization problem is approximately solved by means of convex relaxation, with the results of allowing for different types of a priori information to be incorporated in the optimization. In paper C, a sparse subset of data is provided, and a generative model is used to find the location of an unknown number of jammers in a wireless network, with the jammers’ movement in the network being tracked as additional observations become available