Sparse channel estimation based on compressed sensing theory for UWB systems

Català: L'estimació de canal en receptors wireless esdevé un factor determinant a l'hora de incrementar les prestacions dels sistemes sense fils per tal de satisfer les exigències cada vegades més elevades dels consumidors en quant a velocitats de transmissió i qualitat. En aquesta tesi es proposa explotar la "sparsity" que mostren els canals wireless per tal de millorar els clàssics sistemes d'estimació de canal mitjançant les noves teòries de Compressed Sensing. Així doncs, es proposa un nou model freqüencial de senyal on el canal i un nou algoritme de reconstrucció de senyals sparse que redueix la probabilitat de detecció de falsos camins de propagació millorant d'aquesta manera l'estimació de temps d'arribada.Castellano: En los últimos años, la revolución inalámbrica se ha convertido en una realidad. Wi-fi está en todas partes, impactando significativamente en nuestro estilo de vida. Sin embargo, las comunicaciones inalámbricas nunca tendrán las condiciones de propagación igual que los cables debido a las duras condiciones de la propagación inalámbricas. El canal de radio móvil se caracteriza por la recepción múltiple, eso es que la señal recibida no sólo contiene una camino de propagación, sino también un gran número de ondas reflejadas. Estas ondas reflejadas interfieren con la onda directa, lo que provoca una degradación significativa del rendimiento del enlace. Un sistema inalámbrico debe estar diseñado de tal manera que el efecto adverso del desvanecimiento multicamino sea reducido al mínimo. Afortunadamente, el multipath puede ser visto como diversidad de información dependiendo de la cantidad de Channel State Information (CSI) disponible para el sistema. Sin embargo, en la práctica CSI rara vez se dispone a priori y debe ser estimado. Por otro lado, un canal inalámbrico a menudo puede ser modelado como un canal sparse, en la que el retraso de propagación puede ser muy grande, pero el número de caminos de propagación es normalmente muy pequeño. El conocimiento previo de la sparsity del canal se puede utilizar eficazmente para mejorar la estimación de canal utilizando la nueva teoría de Compressed Sensing (CS). CS se origina en la idea de que no es necesario invertir una gran cantidad de energía en la observación de las entradas de una señal sparse porque la mayoría de ellas será cero. Por lo tanto, CS proporciona un marco sólido para la reducción del número de medidas necesarias para resumir señales sparse. La estimación de canal sparse se centra en este trabajo en Ultra-Wideband (UWB) porque la gran resolución temporal que proporcionan las señales UWB se traduce en un número muy grande de componentes multipath que se pueden resolver. Por lo tanto, UWB mitiga significativamente la distorsión de trayectoria múltiple y proporciona la diversidad multicamino. Esta diversidad junto con la resolución temporal de las señales UWB crear un problema de estimación de canal muy interesante. En esta tesis se estudia el uso de CS en la estimación de canal altamente sparse por medio de un nuevo enfoque de estimación basado en el modelo de frecuencial de la señal UWB. También se propone un nuevo algoritmo llamado extended Orthogonal Matching Pursuit (eOMP) basado en los mismos principios que el clásico OMP, con el fin de mejorar algunas de sus característica.English: In recent years, the wireless revolution has become a reality. Wireless is everywhere having significant impact on our lifestyle. However, wireless will never have the same propagation conditions as wires due to the harsh conditions of the wireless propagation. The mobile radio channel is characterized by multipath reception, that is the signal offered to the receiver contains not only a direct line-of-sight radio wave, but also a large number of reflected radio waves. These reflected waves interfere with the direct wave, which causes significant degradation of the performance of the link. A wireless system has to be designed in such way that the adverse effect of multipath fading is minimized. Fortunately, multipath can be seen as a blessing depending on the amount of Channel State Information (CSI) available to the system. However, in practise CSI is seldom available a priori and needs to be estimated. On the other hand, a wireless channel can often be modeled as a sparse channel in which the delay spread could be very large, but the number of significant paths is normally very small. The prior knowledge of the channel sparseness can be effectively use to improve the channel estimation using the novel Compressed Sensing (CS) theory. CS originates from the idea that is not necessary to invest a lot of power into observing the entries of a sparse signal because most of them will be zero. Therefore, CS provides a robust framework for reducing the number of measurement required to summarize sparse signals. The sparse channel estimation here is focused on Ultra-WideBand (UWB) systems because the very fine time resolution of the UWB signal results in a very large number of resolvable multipath components. Consequently, UWB significantly mitigates multipath distortion and provides path diversity. The rich multipath coupled with the fine time resolution of the UWB signals create a challenging sparse channel estimation problem. This Master Thesis examines the use of CS in the estimation of highly sparse channel by means of a new sparse channel estimation approach based on the frequency domain model of the UWB signal. It is also proposed a new greedy algorithm named extended Orthogonal Matching Pursuit (eOMP) based on the same principles than classical Orthogonal Matching Pursuit (OMP) in order to improve some OMP characteristics. Simulation results show that the new eOMP provides lower false path detection probability compared with classical OMP, which also leads to a better TOA estimation without significant degradation of the channel estimation. Simulation results will also show that the new frequency domain sparse channel model outperforms other models presented in the literature

Wireless Positioning Applications in Multipath Environments

Funklokalisierung in der Umgebung mit der Mehrwegeausbreitung In den vergangenen Jahren wurde zunehmend Forschung im Bereich drahtlose Sensornetzwerk (engl. „Wireless Sensor Network“) betrieben. Lokalisierung im Innenraum ist ein vielversprechendes Forschungsthema, das in den Literaturen vielfältig diskutiert wird. Jedoch berücksichtigen die meisten Arbeiten einen wichtigen Faktor nicht, nämlich die Mehrwegeausbreitung, welche die Genauigkeit der Lokalisierung beeinflusst. Diese Arbeit bezieht sich auf Lokalisierungsanwendungen in UWB (Ultra-Breitband-Technologie)- und WLAN (drahtloses lokales Netzwerk)- Systemen im Fall von Mehrwegeausbreitung. Zur Steigerung der Robustheit der Lokalisierungsanwendungen bei Mehrwegeausbreitung wurden neuartige Lokalisierungsalgorithmen, die auf der Auswertung der Ankunftszeit (engl. „Time of Arrival“, ToA), der empfangenen Signalstärke (engl. „Received Signal Strength“, RSS) und dem Einfallswinkel (engl. „Angle of Arrival“, AoA) basieren, vorgestellt und untersucht. Bei Mehrwegeausbreitung ist die Fragen den direkten Pfad zu lösen, da der direkte Pfad (engl. „Direct Path“, DP) schwächer als anderer Pfad sein kann. In dieser Arbeit werden daher neuartige Algorithmen zur Flankendetektion der empfangenen Signale für UWB Systeme entwickelt, um die Positionsbestimmung zu verbessern: Es gibt die kooperative Flankendetektion (engl. „Joint Leading Edge Detection“, JLED), die erweiterte maximalwahrscheinlichkeitbasierte Kanalschätzung (engl. „Improved Maximum Likelihood Channel Estimation“, IMLCE) und die Flankendetektion mit untervektorraumbasiertem Verfahren (engl. „Subspace based Approaches“, SbA). Bei der kooperativen Flankendetektion werden zwei Kriterien herangezogen nämlich die minimale Fläche und das minimale mittlere Quadrat des Schätzfehlers (engl. „Minimum Mean Squared Error“, MMSE). Weiterhin wird ein monopulsbasierter Kanalschätzer (engl. „Monopulse based Channel Estimator“, MCE) entwickelt, um die möglicherweise falsche Kombinationen der Flanken (engl. „Leading Edge Combination“, LEC) auszuschließen. Zudem wird in der Arbeit der erweiterte MLCE vorgestellt, der aus einem groben und einem genauen Schätzungsschritt besteht. Bei dem neuartigen untervektorraumbasierten Verfahren werden ein statischer und ein Schwundkanal untersucht. Im ersten Fall wird die Kombination der Rückwärtssuchalgorithmus mit untervektorraumbasierten Verfahren untersucht. Zudem wird im zweiten Fall ein untervektorraumbasierte Verfahren im Frequenzbereich vorgestellt. Für die RSS-basierte Lokalisierung wird ein Fingerabdruckverfahren (engl. „Fingerprint Approach“) und ein neuartiger Entfernungsschätzer basierend auf der Kanalenergie entwickelt und implementiert. Schließlich wird in der Arbeit ein Lokalisierungssystem mit Winkelschätzern inklusive einer entsprechenden Kalibrierung auf einer 802.11a/g Hardwareplattform vorgestellt. Dazu wird ein neuartiger Trägerschätzer und Kanalschätzer entwickelt.In the past several years there has been more growing research on Wireless Sensor Network (WSN). The indoor localization is a promising research topic, which is discussed variously in some literatures. However, the most work does not consider an important factor, i.e. the multi-path propagation, which affects the accuracy of the indoor localization. This work dealt with the indoor localization applied in UWB (Ultra Wide Band) and WLAN (Wireless Local Area Network) systems in the case of multi-path propagation. To improve the robustness of the applications of localization in the case of multi-path propagation, novel localization algorithms based on the evaluation of the Time of Arrival (ToA), the Received Signal Strength (RSS) and the Angle of Arrival (AoA) were proposed and investigated. In the ToA based localization systems, the detection of shortest signal propagation time plays a critical role. In the case of multi-path propagation, the Direct Path (DP) needs to be resolved because the DP may be weaker than Multi Path Components (MPC). Thus the novel algorithms for leading edge detection were developed in this work in order to improve the accuracy of localization, namely Joint Leading Edge Detection (JLED), Improved Maximum Likelihood Channel Estimation (IMLCE) and the leading edge detection with Subspace based Approaches (SbA). Two criteria were proposed and referenced for the JLED, namely Minimum Area (MA) and Minimum Mean Squared Error (MMSE). Furthermore, a monocycle-based channel estimator was developed to mitigate the fake LECs (Leading Edge Combination). The estimation error of JLED was theoretically analyzed and simulated for evaluation of the estimator. IMLCE consists of a coarse and a fine estimation step. The coarse position of the first correlation peak shall be found with the Search Back Algorithms (SBA), which is followed by MLCE-algorithms. The novel SbA was investigated in a static and a fading channel. In the former case, the iterative algorithm, which combines SbA with SBA, was investigated. In the latter case, the FD-SbA (Frequency Domain - SbA) was proposed, which requires to calculate the covariance matrix in the FD. For the RSS based localization, fingerprint approach and the novel channel energy based distance estimator were investigated and developed in this dissertation. Finally, a localization system using AoA estimation and the initial calibration was presented on an 802.11a/g hardware platform. A novel Carrier Frequency Offset (CFO) estimator and channel estimator were investigated and developed. The measurement campaigns were made for one, two and four fixed stations, respectivel

Near Optimal Indoor Localization With Coherent Array Reconciliation Tomography

Our increased reliance on localization devices such as GPS navigation has led to an increased demand for localization solutions in all environments, including indoors. Indoor localization has received considerable attention in the last several years for a number of application areas including first responder localization to targeted advertising and social networking. The difficult multipath encountered indoors degrades the performance of RF based localization solutions and so far no optimal solution has been published. This dissertation presents an algorithm called Coherent Array Reconciliation Tomography (CART), which is a Direct Positioning Algorithm (DPA) that incorporates signal fusion to perform a simultaneous leading edge and position estimate for a superior localization solution in a high multipath environment. The CART algorithm produces position estimates that are near optimal in the sense that they achieve nearly the best theoretical accuracy possible using an Impulse Radio (IR) Ultra-Wideband (UWB) waveform. Several existing algorithms are compared to CART including a traditional two step Leading Edge Detection (LED) algorithm, Singular value Array Reconciliation Tomography (SART), and Transactional Array Reconciliation Tomography (TART) by simulation and experimentation. As shown under heavy simulated multipath conditions, where traditional LED produces a limited solution and the SART and TART algorithms fail, the CART algorithm produces a near statistically optimal solution. Finally, the CART algorithm was also successfully demonstrated experimentally in a laboratory environment by application to the fire fighter homing device that has been a part of the ongoing research at Worcester Polytechnic Institute (WPI)

UWB sensor based indoor LOS/NLOS localization with support vector machine learning

Ultra-wideband (UWB) sensor technology is known to achieve high-precision indoor localization accuracy in line-of-sight (LOS) environments, but its localization accuracy and stability suffer detrimentally in non-line-of-sight (NLOS) conditions. Current NLOS/LOS identification based on channel impulse response’s (CIR) characteristic parameters (CCP) improves location accuracy, but most CIR-based identification approaches did not sufficiently exploit the CIR information and are environment specific. This paper derives three new CCPs and proposes a novel two-step identification/classification methodology with dynamic threshold comparison (DTC) and the fuzzy credibility-based support vector machine (FC-SVM). The proposed SVM based classification methodology leverages on the derived CCPs obtained from the waveform and its channel analysis, which are more robust to environment and obstacles dynamic. This is achieved in two-step with a coarse-grained NLOS/LOS identification with the DTC strategy followed by FC-SVM to give the fine-grained result. Finally, based on the obtained identification results, a real-time ranging error mitigation strategy is then designed to improve the ranging and localization accuracy. Extensive experimental campaigns are conducted in different LOS/NLOS scenarios to evaluate the proposed methodology. The results show that the mean LOS/NLOS identification accuracy in various testing scenarios is 93.27 %, and the LOS and NLOS recalls are 94.27 % and 92.57 %, respectively. The ranging errors in LOS(NLOS) conditions are reduced from 0.106 m(1.442 m) to 0.065 m(0.739 m), demonstrating an improvement of 38.85 %(48.74 %) with 0.041 m(0.703 m) error reduction. On the other hand, the average positioning accuracy is also reduced from 0.250 m to 0.091 m with an improvement of 63.49 %(0.159 m), which outperforms the state-of-the-art approaches of the Least-squares support vector machine (LS-SVM) and K-Nearest Neighbor (KNN) algorithms

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Seamless Positioning and Navigation in Urban Environment

L'abstract è presente nell'allegato / the abstract is in the attachmen

Models and Algorithms for Ultra-Wideband Localization in Single- and Multi-Robot Systems

Location is a piece of information that empowers almost any type of application. In contrast to the outdoors, where global navigation satellite systems provide geo-spatial positioning, there are still millions of square meters of indoor space that are unaccounted for by location sensing technology. Moreover, predictions show that people’s activities are likely to shift more and more towards urban and indoor environments– the United Nations predict that by 2020, over 80% of the world’s population will live in cities. Meanwhile, indoor localization is a problem that is not simply solved: people, indoor furnishings, walls and building structures—in the eyes of a positioning sensor, these are all obstacles that create a very challenging environment. Many sensory modalities have difficulty in overcoming such harsh conditions when used alone. For this reason, and also because we aim for a portable, miniaturizable, cost-effective solution, with centimeter-level accuracy, we choose to solve the indoor localization problem with a hybrid approach that consists of two complementary components: ultra-wideband localization, and collaborative localization. In pursuit of the final, hybrid product, our research leads us to ask what benefits collaborative localization can provide to ultra-wideband localization—and vice versa. The road down this path includes diving into these orthogonal sub-domains of indoor localization to produce two independent localization solutions, before finally combining them to conclude our work. As for all systems that can be quantitatively examined, we recognize that the quality of our final product is defined by the rigor of our evaluation process. Thus, a core element of our work is the experimental setup, which we design in a modular fashion, and which we complexify incrementally according to the various stages of our studies. With the goal of implementing an evaluation system that is systematic, repeatable, and controllable, our approach is centered around the mobile robot. We harness this platform to emulate mobile targets, and track it in real-time with a highly reliable ground truth positioning system. Furthermore, we take advantage of the miniature size of our mobile platform, and include multiple entities to form a multi-robot system. This augmented setup then allows us to use the same experimental rigor to evaluate our collaborative localization strategies. Finally, we exploit the consistency of our experiments to perform cross-comparisons of the various results throughout the presented work. Ultra-wideband counts among the most interesting technologies for absolute indoor localization known to date. Owing to its fine delay resolution and its ability to penetrate through various materials, ultra-wideband provides a potentially high ranging accuracy, even in cluttered, non-line-of-sight environments. However, despite its desirable traits, the resolution of non-line-of-sight signals remains a hard problem. In other words, if a non-line-of-sight signal is not recognized as such, it leads to significant errors in the position estimate. Our work improves upon state-of-the-art by addressing the peculiarities of ultra-wideband signal propagation with models that capture the spatiality as well as the multimodal nature of the error statistics. Simultaneously, we take care to develop an underlying error model that is compact and that can be calibrated by means of efficient algorithms. In order to facilitate the usage of our multimodal error model, we use a localization algorithm that is based on particle filters. Our collaborative localization strategy distinguishes itself from prior work by emphasizing cost-efficiency, full decentralization, and scalability. The localization method is based on relative positioning and uses two quantities: relative range and relative bearing. We develop a relative robot detection model that integrates these measurements, and is embedded in our particle filter based localization framework. In addition to the robot detection model, we consider an algorithmic component, namely a reciprocal particle sampling routine, which is designed to facilitate the convergence of a robot’s position estimate. Finally, in order to reduce the complexity of our collaborative localization algorithm, and in order to reduce the amount of positioning data to be communicated between the robots, we develop a particle clustering method, which is used in conjunction with our robot detection model. The final stage of our research investigates the combined roles of collaborative localization and ultra-wideband localization. Numerous experiments are able to validate our overall localization strategy, and show that the performance can be significantly improved when using two complementary sensory modalities. Since the fusion of ultra-wideband positioning sensors with exteroceptive sensors has hardly been considered so far, our studies present pioneering work in this domain. Several insights indicate that collaboration—even if through noisy sensors—is a useful tool to reduce localization errors. In particular, we show that our collaboration strategy can provide the means to minimize the localization error, given that the collaborative design parameters are optimally tuned. Our final results show median localization errors below 10 cm in cluttered environments