Statistical Nested Sensor Array Signal Processing

Source number detection and direction-of-arrival (DOA) estimation are two major applications of sensor arrays. Both applications are often confined to the use of uniform linear arrays (ULAs), which is expensive and difficult to yield wide aperture. Besides, a ULA with N scalar sensors can resolve at most N − 1 sources. On the other hand, a systematic approach was recently proposed to achieve O(N 2 ) degrees of freedom (DOFs) using O(N) sensors based on a nested array, which is obtained by combining two or more ULAs with successively increased spacing. This dissertation will focus on a fundamental study of statistical signal processing of nested arrays. Five important topics are discussed, extending the existing nested-array strategies to more practical scenarios. Novel signal models and algorithms are proposed. First, based on the linear nested array, we consider the problem for wideband Gaussian sources. To employ the nested array to the wideband case, we propose effective strategies to apply nested-array processing to each frequency component, and combine all the spectral information of various frequencies to conduct the detection and estimation. We then consider the practical scenario with distributed sources, which considers the spreading phenomenon of sources. Next, we investigate the self-calibration problem for perturbed nested arrays, for which existing works require certain modeling assumptions, for example, an exactly known array geometry, including the sensor gain and phase. We propose corresponding robust algorithms to estimate both the model errors and the DOAs. The partial Toeplitz structure of the covariance matrix is employed to estimate the gain errors, and the sparse total least squares is used to deal with the phase error issue. We further propose a new class of nested vector-sensor arrays which is capable of significantly increasing the DOFs. This is not a simple extension of the nested scalar-sensor array. Both the signal model and the signal processing strategies are developed in the multidimensional sense. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors. Last but not least, in order to make full use of the available limited valuable data, we propose a novel strategy, which is inspired by the jackknifing resampling method. Exploiting numerous iterations of subsets of the whole data set, this strategy greatly improves the results of the existing source number detection and DOA estimation methods

ARRAY PROCESSING TECHNIQUES FOR ESTIMATION AND TRACKING OF AN ICE-SHEET BOTTOM

Ice bottom topography layers are an important boundary condition required to model the flow dynamics of an ice sheet. In this work, using low frequency multichannel radar data, we locate the ice bottom using two types of automatic trackers. First, we use the multiple signal classification (MUSIC) beamformer to determine the pseudo-spectrum of the targets at each range-bin. The result is passed into a sequential tree-reweighted message passing belief-propagation algorithm to track the bottom of the ice in the 3D image. This technique is successfully applied to process data collected over the Canadian Arctic Archipelago ice caps in 2014, and produce digital elevation models (DEMs) for 102 data frames. We perform crossover analysis to self-assess the generated DEMs, where flight paths cross over each other and two measurements are made at the same location. Also, the tracked results are compared before and after manual corrections. We found that there is a good match between the overlapping DEMs, where the mean error of the crossover DEMs is 38±7 m, which is small relative to the average ice-thickness, while the average absolute mean error of the automatically tracked ice-bottom, relative to the manually corrected ice-bottom, is 10 range-bins. Second, a direction of arrival (DOA)-based tracker is used to estimate the DOA of the backscatter signals sequentially from range bin to range bin using two methods: a sequential maximum a posterior probability (S-MAP) estimator and one based on the particle filter (PF). A dynamic flat earth transition model is used to model the flow of information between range bins. A simulation study is performed to evaluate the performance of these two DOA trackers. The results show that the PF-based tracker can handle low-quality data better than S-MAP, but, unlike S-MAP, it saturates quickly with increasing numbers of snapshots. Also, S-MAP is successfully applied to track the ice-bottom of several data frames collected from over Russell glacier in 2011, and the results are compared against those generated by the beamformer-based tracker. The results of the DOA-based techniques are the final tracked surfaces, so there is no need for an additional tracking stage as there is with the beamformer technique

Spatial Identification Methods and Systems for RFID Tags

Disertační práce je zaměřena na metody a systémy pro měření vzdálenosti a lokalizaci RFID tagů pracujících v pásmu UHF. Úvod je věnován popisu současného stavu vědeckého poznání v oblasti RFID prostorové identifikace a stručnému shrnutí problematiky modelování a návrhu prototypů těchto systémů. Po specifikaci cílů disertace pokračuje práce popisem teorie modelování degenerovaného kanálu pro RFID komunikaci. Detailně jsou rozebrány metody měření vzdálenosti a odhadu směru příchodu signálu založené na zpracování fázové informace. Pro účely lokalizace je navrženo několik scénářů rozmístění antén. Modely degenerovaného kanálu jsou simulovány v systému MATLAB. Významná část této práce je věnována konceptu softwarově definovaného rádia (SDR) a specifikům jeho adaptace na UHF RFID, která využití běžných SDR systémů značně omezují. Diskutována je zejména problematika průniku nosné vysílače do přijímací cesty a požadavky na signál lokálního oscilátoru používaný pro směšování. Prezentovány jsou tři vyvinuté prototypy: experimentální dotazovač EXIN-1, měřicí systém založený na platformě Ettus USRP a anténní přepínací matice pro emulaci SIMO systému. Závěrečná část je zaměřena na testování a zhodnocení popisovaných lokalizačních technik, založených na měření komplexní přenosové funkce RFID kanálu. Popisuje úzkopásmové/širokopásmové měření vzdálenosti a metody odhadu směru signálu. Oba navržené scénáře rozmístění antén jsou v závěru ověřeny lokalizačním měřením v reálných podmínkách.The doctoral thesis is focused on methods and systems for ranging and localization of RFID tags operating in the UHF band. It begins with a description of the state of the art in the field of RFID positioning with short extension to the area of modeling and prototyping of such systems. After a brief specification of dissertation objectives, the thesis overviews the theory of degenerate channel modeling for RFID communication. Details are given about phase-based ranging and direction of arrival finding methods. Several antenna placement scenarios are proposed for localization purposes. The degenerate channel models are simulated in MATLAB. A significant part of the thesis is devoted to software defined radio (SDR) concept and its adaptation for UHF RFID operation, as it has its specialties which make the usage of standard SDR test equipment very disputable. Transmit carrier leakage into receiver path and requirements on local oscillator signals for mixing are discussed. The development of three experimental prototypes is also presented there: experimental interrogator EXIN-1, measurement system based on Ettus USRP platform, and antenna switching matrix for an emulation of SIMO system. The final part is focused on testing and evaluation of described positioning techniques based on complex backscatter channel transfer function measurement. Both narrowband/wideband ranging and direction of arrival methods are validated. Finally, both proposed antenna placement scenarios are evaluated with real-world measurements.

Mutual Coupling in Phased Arrays: A Review

The mutual coupling between antenna elements affects the antenna parameters like terminal impedances, reflection coefficients and hence the antenna array performance in terms of radiation characteristics, output signal-to-interference noise ratio (SINR), and radar cross section (RCS). This coupling effect is also known to directly or indirectly influence the steady state and transient response, the resolution capability, interference rejection, and direction-of-arrival (DOA) estimation competence of the array. Researchers have proposed several techniques and designs for optimal performance of phased array in a given signal environment, counteracting the coupling effect. This paper presents a comprehensive review of the methods that model and mitigate the mutual coupling effect for different types of arrays. The parameters that get affected due to the presence of coupling thereby degrading the array performance are discussed. The techniques for optimization of the antenna characteristics in the presence of coupling are also included

Algorithms for propagation-aware underwater ranging and localization

Mención Internacional en el título de doctorWhile oceans occupy most of our planet, their exploration and conservation are one of the crucial research problems of modern time. Underwater localization stands among the key issues on the way to the proper inspection and monitoring of this significant part of our world. In this thesis, we investigate and tackle different challenges related to underwater ranging and localization. In particular, we focus on algorithms that consider underwater acoustic channel properties. This group of algorithms utilizes additional information about the environment and its impact on acoustic signal propagation, in order to improve the accuracy of location estimates, or to achieve a reduced complexity, or a reduced amount of resources (e.g., anchor nodes) compared to traditional algorithms. First, we tackle the problem of passive range estimation using the differences in the times of arrival of multipath replicas of a transmitted acoustic signal. This is a costand energy- effective algorithm that can be used for the localization of autonomous underwater vehicles (AUVs), and utilizes information about signal propagation. We study the accuracy of this method in the simplified case of constant sound speed profile (SSP) and compare it to a more realistic case with various non-constant SSP. We also propose an auxiliary quantity called effective sound speed. This quantity, when modeling acoustic propagation via ray models, takes into account the difference between rectilinear and non-rectilinear sound ray paths. According to our evaluation, this offers improved range estimation results with respect to standard algorithms that consider the actual value of the speed of sound. We then propose an algorithm suitable for the non-invasive tracking of AUVs or vocalizing marine animals, using only a single receiver. This algorithm evaluates the underwater acoustic channel impulse response differences induced by a diverse sea bottom profile, and proposes a computationally- and energy-efficient solution for passive localization. Finally, we propose another algorithm to solve the issue of 3D acoustic localization and tracking of marine fauna. To reach the expected degree of accuracy, more sensors are often required than are available in typical commercial off-the-shelf (COTS) phased arrays found, e.g., in ultra short baseline (USBL) systems. Direct combination of multiple COTS arrays may be constrained by array body elements, and lead to breaking the optimal array element spacing, or the desired array layout. Thus, the application of state-of-the-art direction of arrival (DoA) estimation algorithms may not be possible. We propose a solution for passive 3D localization and tracking using a wideband acoustic array of arbitrary shape, and validate the algorithm in multiple experiments, involving both active and passive targets.Part of the research in this thesis has been supported by the EU H2020 program under project SYMBIOSIS (G.A. no. 773753).This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Paul Daniel Mitchell.- Secretario: Antonio Fernández Anta.- Vocal: Santiago Zazo Bell

Low complexity DOA estimation for wideband off-grid sources based on re-focused compressive sensing with dynamic dictionary

Under the compressive sensing (CS) framework, a novel focusing based direction of arrival (DOA) estimation method is first proposed for wideband off-grid sources, and by avoiding the application of group sparsity (GS) across frequencies of interest, significant complexity reduction is achieved with its computational complexity close to that of solving a single frequency based direction finding problem. To further improve the performance by alleviating both the off-grid approximation errors and the focusing errors which are even worse for the off-grid case, a dynamic dictionary based re-focused off-grid DOA estimation method is developed with the number of extremely sparse grids involved in estimation refined to the number of detected sources, and thus the complexity is still very low due to the limited increased complexity introduced by iterations, while improved performance can be achieved compared with those fixed dictionary based off-grid methods

Cooperative Position and Orientation Estimation with Multi-Mode Antennas

Robotic multi-agent systems are envisioned for planetary exploration and terrestrial applications. Autonomous operation of robots requires estimations of their positions and orientations, which are obtained from the direction-of-arrival (DoA) and the time-of-arrival (ToA) of radio signals exchanged among the agents. In this thesis, we estimate the signal DoA and ToA using a multi-mode antenna (MMA). An MMA is a single antenna element, where multiple orthogonal current modes are excited by different antenna ports. We provide a first study on the use of MMAs for cooperative position and orientation estimation, specifically exploring their DoA estimation capabilities. Assuming the agents of a cooperative network are equipped with MMAs, lower bounds on the achievable position and orientation accuracy are derived. We realize a gap between the theoretical lower bounds and real-world performance of a cooperative radio localization system, which is caused by imperfect antenna and transceiver calibration. Consequentially, we theoretically analyze in-situ antenna calibration, introduce an algorithm for the calibration of arbitrary multiport antennas and show its effectiveness by simulation. To also improve calibration during operation, we propose cooperative simultaneous localization and calibration (SLAC). We show that cooperative SLAC is able to estimate antenna responses and ranging biases of the agents together with their positions and orientations, leading to considerably better position and orientation accuracy. Finally, we validate the results from theory and simulation by experiments with robotic rovers equipped with software-defined radios (SDRs). In conclusion, we show that DoA estimation with an MMA is feasible, and accuracy can be improved by in-situ calibration and SLAC

DOA estimation and mutual coupling calibration with the SAGE algorithm

AbstractIn this paper, a novel algorithm is presented for direction of arrival (DOA) estimation and array self-calibration in the presence of unknown mutual coupling. In order to highlight the relationship between the array output and mutual coupling coefficients, we present a novel model of the array output with the unknown mutual coupling coefficients. Based on this model, we use the space alternating generalized expectation-maximization (SAGE) algorithm to jointly estimate the DOA parameters and the mutual coupling coefficients. Unlike many existing counterparts, our method requires neither calibration sources nor initial calibration information. At the same time, our proposed method inherits the characteristics of good convergence and high estimation precision of the SAGE algorithm. By numerical experiments we demonstrate that our proposed method outperforms the existing method for DOA estimation and mutual coupling calibration

Sensor array signal processing : two decades later

Caption title.Includes bibliographical references (p. 55-65).Supported by Army Research Office. DAAL03-92-G-115 Supported by the Air Force Office of Scientific Research. F49620-92-J-2002 Supported by the National Science Foundation. MIP-9015281 Supported by the ONR. N00014-91-J-1967 Supported by the AFOSR. F49620-93-1-0102Hamid Krim, Mats Viberg