Higher Order Statistics (HOS) and Second Order Statistics (SOS) approaches have certain advantages and disadvantages in signal processing applications. HOS approach provides more statistical information for non-Gaussian signals. On the other hand, SOS approach is more robust to the estimation errors than the HOS approach, especially when the number of observations is small. In this thesis, HOS and SOS approaches are jointly used in order to take advantage of both methods. In this respect, the joint use of HOS and SOS approaches are introduced for online calibration of sensor arrays with arbitrary geometries. Three different problems in online array calibration are considered and new algorithms for each of these problems are proposed. In the first problem, the positions of the randomly deployed sensors are completely unknown except the two reference sensors and HOS and SOS approaches are used iteratively for the joint Direction of Arrival (DOA) and sensor position estimation. Iterative HOS-SOS algorithm (IHOSS) solves the ambiguity problem in sensor position estimation by observing the source signals at least in two different frequencies and hence it is applicable for wideband signals. The conditions on these frequencies are presented. IHOSS is the first algorithm in the literature which finds the DOA and sensor position estimations in case of randomly deployed sensors with unknown coordinates. In the second problem, narrowband signals are considered and it is assumed that the nominal sensor positions are known. Modified IHOSS (MIHOSS) algorithm uses the nominal sensor positions to solve the ambiguity problem in sensor position estimation. This algorithm can handle both small and large errors in sensor positions. The upper bound of perturbations for unambiguous sensor position estimation is presented. In the last problem, an online array calibration method is proposed for sensor arrays where the sensors have unknown gain/phase mismatches and mutual coupling coefficients. In this case, sensor positions are assumed to be known. The mutual coupling matrix is unstructured. The two reference sensors are assumed to be perfectly calibrated. IHOSS algorithm is adapted for online calibration and parameter estimation, and hence CIHOSS algorithm is obtained. While CIHOSS originates from IHOSS, it is fundamentally different in many aspects. CIHOSS uses multiple virtual ESPRIT structures and employs an alignment technique to order the elements of rows of the actual array steering matrix. In this thesis, a new cumulant matrix estimation technique is proposed for the HOS approach by converting the multi-source problem into a single source one. The proposed algorithms perform well even in the case of correlated source signals due to the effectiveness of the proposed cumulant matrix estimate. The iterative procedure in all the proposed algorithms is guaranteed to converge. Closed form expressions are derived for the deterministic Cram&acute / er-Rao bound (CRB) for DOA and unknown calibration parameters for non-circular complex Gaussian noise with unknown covariance matrix. Simulation results show that the performances of the proposed methods approach to the CRB for both DOA and unknown calibration parameter estimations for high SNR.

Signal acquisition, signal reconstruction and analysis of spectrum of the signal are the three most important steps in signal processing and they are found in almost all of the modern day hardware. In most of the signal processing hardware, the signal of interest is sampled at uniform intervals satisfying some conditions like Nyquist rate. However, in some cases the privilege of having uniformly sampled data is lost due to some constraints on the hardware resources. In this thesis an important problem of signal reconstruction and spectral analysis from nonuniformly sampled data is addressed and a variety of methods are presented. The proposed methods are tested via numerical experiments on both artificial and real-life data sets. The thesis starts with a brief review of methods available in the literature for signal reconstruction and spectral analysis from non uniformly sampled data. The methods discussed in the thesis are classified into two broad categories - dense and sparse methods, the classification is based on the kind of spectra for which they are applicable. Under dense spectral methods the main contribution of the thesis is a non-parametric approach named LIMES, which recovers the smooth spectrum from non uniformly sampled data. Apart from recovering the spectrum, LIMES also gives an estimate of the covariance matrix. Under sparse methods the two main contributions are methods named SPICE and LIKES - both of them are user parameter free sparse estimation methods applicable for line spectral estimation. The other important contributions are extensions of SPICE and LIKES to multivariate time series and array processing models, and a solution to the grid selection problem in sparse estimation of spectral-line parameters. The third and final part of the thesis contains applications of the methods discussed in the thesis to the problem of radial velocity data analysis for exoplanet detection. Apart from the exoplanet application, an application based on Sudoku, which is related to sparse parameter estimation, is also discussed.

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No. of bitstreams: 2 Dissertação - Jonas Augusto Kunzler - 2015.pdf: 5934373 bytes, checksum: a736817202816bba60673f1a39184580 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2015-04-14 / New technologies that have emerged from the transistor advent enabled digital signal processing techniques were aggregated to systems operated or partially in analog improving performance of these systems. Added to this the use of sensor arrangements which made it possible to identify the directions of signals of interest and are used in critical areas of knowledge, such as in tracking radar systems; in astronomy; in sonar systems; mobile communications; the estimation of direction of arrival; in seismology and medical diagnosis and treatment. This work aims to study specific features of an estimation method of direction of arrival based on a linear array of sensors with special attention to mobile communications. Some methods have been proposed in order to get the position of a source of electromagnetic waves and can cite the MUSIC, the MODEX and SEAD, the latter of fundamental importance to this work because it is the basis of conducted research and because it is a new method, lacked further clarification with regard to the differential spectrum, their origin, meaning and importance, as well as obtaining an analytical expression to describe their conduct on the variables that compose the system. Based on eigenvalue decomposition of the correlation matrix has been observed that the differential spectrum is basically a matrix norm calculation, with the largest eigenvalue of the matrix is ​​also standard 2-induced vector. It was proposed to use the Frobenius norm which is simpler to be computed, and consequently requires less computational effort. Moreover, the behavior of the angular spectrum calculated using the Frobenius norm is fully described by the sum of cosines with the formulation described for each part which composes the calculation of the standard. Through this outcome was possible to analyze aspects related to angular resolution, the number of signal sources, the number of sensors, the influence of noise and the correlation between sources. / Novas tecnologias que surgiram a partir do advento transistor permitiram técnicas de processamento digital de sinais fossem agregadas a sistemas que operavam ou parcialmente de forma analógica aprimorando o desempenho desses sistemas. Soma-se a isto a utilização de arranjos de sensores que possibilitaram a identificação das direções dos sinais de interesse e são empregados em importantes áreas do conhecimento, como por exemplo, em sistemas de rastreamento por radar; na Astronomia; em Sistemas sonares; nas comunicações móveis; na estimação de direção de chegada; na sismologia e no diagnóstico e tratamento médico. Este trabalho tem como objetivo estudar características específicas de um método de estimação de direção de chegada baseado em um arranjo linear de sensores com atenção especial em comunicações móveis. Alguns métodos foram propostos com o fim de obter a posição de uma fonte de ondas eletromagnéticas, podendo-se citar o MUSIC, o MODEX e o SEAD, este último de fundamental importância para este trabalho, pois, ele é a base da investigação conduzida e por se tratar de um método novo, carecia de mais esclarecimentos no que diz respeito ao espectro diferencial, sua origem, significado e importância, como também a obtenção de uma expressão analítica que descrevesse seu comportamento em função das variáveis que compões o sistema. Baseado na decomposição em autovalores da matriz de correlação observou-se que o espectro diferencial é basicamente um cálculo de norma matricial, sendo que o maior autovalor da matriz é também a norma 2 induzida por vetor. Propôs-se a utilização da norma de Frobenius que é mais simples de ser calculada e, consequentemente, exige menos esforço computacional. Além disso, o comportamento do espectro angular calculado com a norma de Frobenius é totalmente descrito através da soma de cossenos com a formulação descrita para cada parcela que compõe o cálculo da norma. Através deste resultado foi possível analisar aspectos referentes à resolução angular, ao número de fontes de sinal, ao número de sensores, à influência do ruído e à correlação entre as fontes.

No description available.

Un radar MIMO (Multiple Input Multiple Output) est un système radar qui utilise plusieurs antennes émettrices et réceptrices, dans lequel les formes d'ondes émises peuvent être indépendantes. Par rapport aux radars utilisant des antennes en réseaux phasés, les radars MIMO offrent davantage de degrés de liberté, ce qui permet d'améliorer les performances du système en termes de détection et localisation. La technique MIMO offre également la possibilité de synthétiser un diagramme de rayonnement désiré par une définition judicieuse des formes d'ondes émises. Dans la mesure où les paramètres des cibles (positions, vitesses, directions d'arrivée (DOA), ...) sont estimés à partir des échos des signaux émis, on comprend aisément que les formes d'ondes employées jouent un rôle clé dans les performances du système. Cette thèse porte sur l'estimation de DOA et sur la conception des formes d'ondes pour un radar MIMO. Le cadre d'étude est restreint au cas où les antennes sont colocalisées et les cibles sont immobiles et supposées ponctuelles. La plupart des travaux antérieurs (au commencement de la thèse) portaient sur le radar MIMO bande étroite et faisaient l'hypothèse d'émetteurs-récepteurs idéaux et indépendants. Cette thèse contribue à élargir le cadre d'étude en s'intéressant d'une part au passage en large bande et d'autre part à la modélisation et à la prise en compte de la non-indépendance des émetteurs-récepteurs et autres imperfections. Dans la mesure où le recours à des signaux large bande est nécessaire lorsqu'une résolution importante est souhaitée, nous nous sommes attachés dans cette thèse à adapter le modèle d'un système de radar MIMO au cas large bande et à proposer de nouvelles techniques visant à améliorer les performances d'estimation de DOA dans le cas de signaux large bande. Cette thèse analyse également l'influence de conditions non idéales comme l'impact des phénomènes de couplage électromagnétique sur les diagrammes de rayonnement dans un réseau d'antennes. Cette étude est menée dans le cas bande étroite. En particulier, nous étudions l'influence du couplage direct entre les réseaux d'antennes d'émission et de réception (appelé « crosstalk ») sur les performances des techniques proposées. Nous établissons un modèle du signal permettant de prendre en compte ce phénomène et proposons une technique de réduction du « crosstalk » qui permet une estimation efficace des DOA des cibles. Nous montrons par ailleurs comment améliorer les performances d'estimation de DOA en présence de diagrammes de rayonnement incluant le couplage entre antennes. Le dernier apport principal de cette thèse est la conception et réalisation d'une plateforme expérimentale comportant une seule architecture d'émetteur-récepteur, qui permet de simuler un système MIMO utilisant des antennes colocalisées en appliquant le principe de superposition. Cette plateforme nous a permis d'évaluer les performances des techniques proposées dans des conditions plus réalistes / A Multiple-Input Multiple-Output (MIMO) radar is a system employing multiple transmitters and receivers in which the waveforms to be transmitted can be totally independent. Compared to standard phased-array radar systems, MIMO radars offer more degrees of freedom which leads to improved angular resolution and parameter identifiability, and provides more flexibility for transmit beampattern design. The main issues of interest in the context of MIMO radar are the estimation of several target parameters (which include range, Doppler, and Direction-of-Arrival (DOA), among others). Since the information on the targets is obtained from the echoes of the transmitted signals, it is straightforward that the design of the waveforms plays an important role in the system accuracy. This document addresses the investigation of DOA estimation of non-moving targets and waveform design techniques for MIMO radar with colocated antennas. Although narrowband MIMO radars have been deeply studied in the literature, the existing DOA estimation techniques have been usually proposed and analyzed from a theoretical point of view, often assuming ideal conditions. This thesis analyzes existing signal processing algorithms and proposes new ones in order to improve the DOA estimation performance in the case of narrowband and wideband signals. The proposed techniques are studied under ideal and non-ideal conditions considering punctual targets. Additionally, we study the influence of mutual coupling on the performance of the proposed techniques and we establish a more realistic signal model which takes this phenomenon into account. We then show how to improve the DOA estimation performance in the presence of distorted radiation patterns and we propose a crosstalk reduction technique, which makes possible an efficient estimation of the target DOAs. Finally, we present an experimental platform for MIMO radar with colocated antennas which has been developed in order to evaluate the performance of the proposed techniques under more realistic conditions. The proposed platform, which employs only one transmitter and one receiver architectures, relies on the superposition principle to simulate a real MIMO system

Direction of Arrival estimation using unsteered antenna arrays, unlike mechanically scanned or phased arrays, requires complex algorithms which perform poorly with small aperture arrays or without a large number of observations, or snapshots. In general, these algorithms compute a sample covriance matrix to obtain the direction of arrival and some require a prior estimate of the number of signal sources. Herein, artificial neural network architectures are proposed which demonstrate improved estimation of the number of signal sources, the true signal covariance matrix, and the direction of arrival. The proposed number of source estimation network demonstrates robust performance in the case of coherent signals where conventional methods fail. For covariance matrix estimation, four different network architectures are assessed and the best performing architecture achieves a 20 times improvement in performance over the sample covariance matrix. Additionally, this network can achieve comparable performance to the sample covariance matrix with 1/8-th the amount of snapshots. For direction of arrival estimation, preliminary results are provided comparing six architectures which all demonstrate high levels of accuracy and demonstrate the benefits of progressively training artificial neural networks by training on a sequence of sub- problems and extending to the network to encapsulate the entire process.

Poaching is a persistent issue that threatens many of earth’s species including therhino. The methods used by poachers are varied, but many use guns to carry outtheir illegal activities. Gunfire is extremely loud and can be heard for kilometres.This thesis investigates whether it is possible to aid anti-poaching efforts in Kenyawith a gunshot detection and estimation device using an array of microphones. Ifsuccessful, the device could be placed around the savannah or any exposed areaand warn if poaching is taking place in the nearby. If a shot is fired within theaudible range of the device’s microphones, a trained machine learning algorithmdetects the shot on the edge using a microprocessor. The detection runs in realtime and achieved an accuracy of 93% on an unbalanced data set, where themajority class was the one without gunshots. Once a detection has been made, thereceived signal power to each microphone is used to produce a direction of arrivalestimate. The estimate can produce an angle estimate with a standard deviationof 66.78° for a gunshot, and with a standard deviation of 7.65° when testing themodel with white noise. Future implementations could use several devices thatdetected the same event, and fuse their estimates to locate the shooter’s position.All of this information, as well as the sound file, can be used to alert and assistlocal wildlife services. The challenges of this project have been centred aroundmaking a system run in real time with only a microprocessor on the edge, whilealso prioritizing low cost components for future deployment. / Project Ngulia

The angle of arrival estimation of multiple sources plays a vital role in the field of array signal processing as MIMO systems can be employed at both the transmitter and the receiver end and the system capacity, reliability and throughput can be significantly increased by using array signal processing. Almost all applications require accurate direction of arrival (DOA) estimation to localize the sources of the signals. Another important parameter of localization systems is the array geometry and sensor design which can be application specific and is used to estimate the DOA. In this work, various array geometries and arrival estimation algorithms are studied and then a new scheme for multiple source estimation is proposed and evaluated based on the performance of subspace and non-subspace decomposition methods. The proposed scheme has shown to outperform the conventional Multiple Signal Classification (MUSIC) estimation and Bartlett estimation techniques. The new scheme has a better performance advantage at low and high signal to noise ratio values (SNRs). The research work also studies different array geometries for both single and multiple incident sources and proposes a geometry which is cost effective and efficient for 3, 4, and 5 antenna array elements. This research also considers the shape of the ground plane and its effects on the angle of arrival estimation and in addition it shows how the mutual couplings between the elements effect the overall estimation and how this error can be minimised by using a decoupling matrix. At the end, a novel miniaturised multi element reconfigurable antenna to represent the receiver base station is designed and tested. The antenna radiation patterns in the azimuth angle are almost omni-directional with linear polarisation. The antenna geometry is uniplanar printed logspiral with striplines feeding network and biased components to improve the impedance bandwidth. The antenna provides the benefit of small size, and re-configurability and is very well suited for the asset tracking applications.

Sensor network performance is governed by the physical placement of sensors and their geometric relationship to the events they measure. To illustrate this, the entirety of this thesis covers the following interconnected subjects: 1) graphical analysis of the variance of the estimation error caused by physical characteristics of an acoustic target source and its geometric location relative to sensor arrays, 2) event/anomaly detection method for time aggregated point sensor data using a parametric Poisson distribution data model, 3) a sensor reduction or placement technique using Bellman optimal estimates of target agent dynamics and probabilistic training data (Goode, Chin, & Roan, 2011), and 4) transforming event monitoring point sensor data into event detection and classification of the direction of travel using a contextual, joint probability, causal relationship, sliding window, and geospatial intelligence (GEOINT) method. / Master of Science

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / Este trabalho se propÃe a investigar e propor diferentes tÃcnicas de estimaÃÃo de canal de mÃltiplas entradas e mÃltiplas saÃdas (MIMO) para sistemas de comunicaÃÃo multiusuÃrio operando em regime de interferÃncia em cenÃrio de mÃltiplas cÃlulas. AtenÃÃo particular à dada ao caso onde as estaÃÃes rÃdio-base sÃo equipadas com arranjos de antenas apresentando grande quantidade de antenas, configurando o que se tem referido na literatura como sistemas de comunicaÃÃo MIMO de grande dimensÃo (VL-MIMO, do inglÃs: very large MIMO). Algumas destas tÃcnicas exploram as propriedades das grandes matrizes aleatÃrias e sÃo menos afetadas pela contaminaÃÃo de pilotos. Nesta dissertaÃÃo, os parÃmetros do canal VL-MIMO sÃo estimados a partir de uma decomposiÃÃo em autovalores (EVD, do inglÃs: eigenvalue-decomposition) da matriz de covariÃncia na saÃda do arranjo de antenas receptoras. Esta tÃcnica se mostra menos sensÃvel à presenÃa de interferÃncia do que outras que nÃo exploram propriedades especÃficas da matriz de canal VL-MIMO, como à o caso da soluÃÃo clÃssica dos mÃnimos quadrados (LS, do inglÃs: least-squares). Nesse contexto, propÃe-se ainda uma soluÃÃo para o fator de ambiguidade multiplicativa do mÃtodo baseado em EVD, utilizando um simples produto de Khatri-Rao. Na segunda parte desta dissertaÃÃo, as propriedades dos sistemas VL-MIMO sÃo empregadas num problema de localizaÃÃo de fontes, a fim de determinar a direÃÃo de chegada (DOA) dos sinais incidentes sobre o arranjo, provenientes da cÃlula em questÃo. Explorando o subespaÃo de representaÃÃo dos sinais interferentes, propÃe-se o uso de um algoritmo de classificaÃÃo de tipo MUSIC para estimar a matriz de canal de forma cega. O mÃtodo proposto converte os altos ganhos de resoluÃÃo dos arranjos VL-MIMO em capacidade de reduÃÃo de interferÃncia, podendo fornecer estimativas do canal adequadas, mesmo sob nÃveis fortes de interferÃncia e tambÃm em casos onde os sinais do usuÃrio desejado e dos interferentes sÃo altamente correlacionados espacialmente. Extensas campanhas de simulaÃÃo computacional foram realizadas, dandoum carÃter exploratÃrio a esta dissertaÃÃo no sentido de abranger diferentes cenÃrios e avaliar as tÃcnicas investigadas em comparaÃÃo com soluÃÃes jà consolidadas, permitindo assim a elaboraÃÃo de um panorama mais completo de caracterizaÃÃo dos problemas de estimaÃÃo de parÃmetros no caso VL-MIMO. / The aim of this dissertation is mainly to investigate and propose different channel estimation techniques for a multicell multiuser multiple-input multiple-output (MIMO) communication system. Particular attention is payed to the case that is referred to as very large (VL) MIMO (VL-MIMO) arrays, where the base stations are equipped with a great (or even huge) number of antenna sensors. Some of these techniques exploit properties issued from the (large) Random Matrices Theory and are therefore less affected by the so-called pilot contamination effect. In this work, the parameters of the VL-MIMO channel are estimated from the eigenvalue decomposition (EVD) of the output covariance matrix of the receive antenna array. This technique is more robust to the interference of signals from other cells compared with methods that do not exploit the specific properties of the VL-MIMO channel matrix, which is the case of the classical least squares (LS) solution. In this context, this work also proposes a simpler way to resolve the scaling ambiguity remaining from the EVD-based method using the Khatri-Rao product. The second part of this dissertation exploits the VL-MIMO properties on a source localization problem, aiming to determine the direction of arrival (DoA) of the signals impinging on the antenna array from a given desired cell. Based on the subspace representation of the outer cell interference signals, we propose a new blind MUSIC-like classification algorithm to estimate the channel matrix. The proposed technique convert the high resolution gains of the VL-MIMO arrays into ability to reduce power of undesired signals, yielding good channel estimates even under high interference power levels, and including cases where desired and undesired signals are strongly correlated. Computer simulations have been done in order to cope with different situations and propagation scenarios, thus yielding an exploratory character to our research and allowing us to evaluate and assess the investigated algorithms, comparing them to consolidated solutions in order to establish a complete overview of the parameter estimation problem in the VL-MIMO case.