Estimation of the Number of Sources in Unbalanced Arrays via Information Theoretic Criteria

Estimating the number of sources impinging on an array of sensors is a well known and well investigated problem. A common approach for solving this problem is to use an information theoretic criterion, such as Minimum Description Length (MDL) or the Akaike Information Criterion (AIC). The MDL estimator is known to be a consistent estimator, robust against deviations from the Gaussian assumption, and non-robust against deviations from the point source and/or temporally or spatially white additive noise assumptions. Over the years several alternative estimation algorithms have been proposed and tested. Usually, these algorithms are shown, using computer simulations, to have improved performance over the MDL estimator, and to be robust against deviations from the assumed spatial model. Nevertheless, these robust algorithms have high computational complexity, requiring several multi-dimensional searches. In this paper, motivated by real life problems, a systematic approach toward the problem of robust estimation of the number of sources using information theoretic criteria is taken. An MDL type estimator that is robust against deviation from assumption of equal noise level across the array is studied. The consistency of this estimator, even when deviations from the equal noise level assumption occur, is proven. A novel low-complexity implementation method avoiding the need for multi-dimensional searches is presented as well, making this estimator a favorable choice for practical applications.Comment: To appear in the IEEE Transactions on Signal Processin

Statistical Performance Analysis of MDL Source Enumeration in Array Processing

In this correspondence, we focus on the performance analysis of the widely-used minimum description length (MDL) source enumeration technique in array processing. Unfortunately, available theoretical analysis exhibit deviation from the simulation results. We present an accurate and insightful performance analysis for the probability of missed detection. We also show that the statistical performance of the MDL is approximately the same under both deterministic and stochastic signal models. Simulation results show the superiority of the proposed analysis over available results.Comment: Accepted for publication in IEEE Transactions on Signal Processing, April 200

Fast and Robust Parametric Estimation of Jointly Sparse Channels

We consider the joint estimation of multipath channels obtained with a set of receiving antennas and uniformly probed in the frequency domain. This scenario fits most of the modern outdoor communication protocols for mobile access or digital broadcasting among others. Such channels verify a Sparse Common Support property (SCS) which was used in a previous paper to propose a Finite Rate of Innovation (FRI) based sampling and estimation algorithm. In this contribution we improve the robustness and computational complexity aspects of this algorithm. The method is based on projection in Krylov subspaces to improve complexity and a new criterion called the Partial Effective Rank (PER) to estimate the level of sparsity to gain robustness. If P antennas measure a K-multipath channel with N uniformly sampled measurements per channel, the algorithm possesses an O(KPNlogN) complexity and an O(KPN) memory footprint instead of O(PN^3) and O(PN^2) for the direct implementation, making it suitable for K << N. The sparsity is estimated online based on the PER, and the algorithm therefore has a sense of introspection being able to relinquish sparsity if it is lacking. The estimation performances are tested on field measurements with synthetic AWGN, and the proposed algorithm outperforms non-sparse reconstruction in the medium to low SNR range (< 0dB), increasing the rate of successful symbol decodings by 1/10th in average, and 1/3rd in the best case. The experiments also show that the algorithm does not perform worse than a non-sparse estimation algorithm in non-sparse operating conditions, since it may fall-back to it if the PER criterion does not detect a sufficient level of sparsity. The algorithm is also tested against a method assuming a "discrete" sparsity model as in Compressed Sensing (CS). The conducted test indicates a trade-off between speed and accuracy.Comment: 11 pages, 9 figures, submitted to IEEE JETCAS special issue on Compressed Sensing, Sep. 201

A unified approach to sparse signal processing

A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. The key application domains of sparse signal processing are sampling, coding, spectral estimation, array processing, compo-nent analysis, and multipath channel estimation. In terms of the sampling process and reconstruction algorithms, linkages are made with random sampling, compressed sensing and rate of innovation. The redundancy introduced by channel coding i

Filtering on DOA estimation using array of sensors

Orientador: Amauri LopesDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: Este trabalho aborda o problema de estimação da direção de chegada (DOA) de ondas planas usando arranjo de sensores. Existem diversos estimadores para DOA relatados na literatura. Dentre os estimadores de alta resolução, se destacam os métodos MODE e MODEX, que possuem como base o estimador de máxima verossimilhança (MLE). Este trabalho apresenta o desenvolvimento dos métodos MODE, MODEX e de uma versão melhorada do MODEX, o método MODEX Modi?ed. Estes dois últimos estimadores produzem várias estimativas candidatas e usam o critério de máxima verossimilhança para selecionar aquelas que representam as melhores estimativas para os ângulos de chegada. Entretanto, para uma relação sinalruído baixa, estes métodos sofrem uma forte degradação na escolha das candidatas. Na busca de reduzir esta degradação, é apresentada uma proposta de ?ltragem nos sinais captados pelos sensores, com o objetivo de melhorar a relação sinalruído. São propostos dois projetos de ?ltro FIR: um por alocação de pólos e zeros, e outro por amostragem em freqüência. Os resultados obtidos mostram que esta proposta de ?ltragem é válida e que se consegue reduzir signi?cativamente a SNR do limiar de desempenho apresentado pelos métodos MODEX e MODEX Modi?ed.Abstract: This work deals with the estimation of the direction of arrival (DOA) of plane waves using array of sensors. There are various estimators for DOA reported in literature. The MODE and MODEX methods, based on the maximum likelihood criterion, are the best high resolution DOA estimators. This work presents the development of these methods as well as of an improved version of the MODEX, named MODEX Modi?ed. MODEX and MODEX Modi?ed produce some estimates that are candidates for the DOA estimation and use the maximum likelihood criterion to select the best ones. However, for low signaltonoise ratio, the selection process suffers a strong performance degradation. In order to reduce this degradation, this work proposes to ?lter the received signals aiming to improve the signaltonoise ratio. Two FIR ?lters are considered: one composed by poles and zeros and another obtained by sampling in the frequency domain. Simulation results show that this proposal improves signi?cantly the performance of both MODEX and MODEX Modi?ed.MestradoTelecomunicações e TelemáticaMestre em Engenharia Elétric