Direction-of-Arrival Estimation for Temporally Correlated Narrowband Signals

signal direction-of-arrival estimation using an array of sensors has been the subject of intensive research and development during the last two decades. Efforts have been directed to both, better solutions for the general data model and to develop more realistic models. So far, many authors have assumed the data to be iid samples of a multivariate statistical model. Although this assumption reduces the complexity of the model, it may not be true in certain situations where signals show temporal correlation. Some results are available on the temporally correlated signal model in the literature. The temporally correlated stochastic Cramer-Rao bound (CRB) has been calculated and an instrumental variable-based method called IV-SSF is introduced. Also, it has been shown that temporally correlated CRB is lower bounded by the deterministic CRB. In this paper, we show that temporally correlated CRB is also upper bounded by the stochastic iid CRB. We investigate the effect of temporal correlation of the signals on the best achievable performance. We also show that the IV-SSF method is not efficient and based on an analysis of the CRB, propose a variation in the method which boosts its performance. Simulation results show the improved performance of the proposed method in terms of lower bias and error variance.Comment: IEEE Transactions on Signal Processing, vol. 57, pp. 600-609, Feb. 200

A CLT on the SNR of Diagonally Loaded MVDR Filters

This paper studies the fluctuations of the signal-to-noise ratio (SNR) of minimum variance distorsionless response (MVDR) filters implementing diagonal loading in the estimation of the covariance matrix. Previous results in the signal processing literature are generalized and extended by considering both spatially as well as temporarily correlated samples. Specifically, a central limit theorem (CLT) is established for the fluctuations of the SNR of the diagonally loaded MVDR filter, under both supervised and unsupervised training settings in adaptive filtering applications. Our second-order analysis is based on the Nash-Poincar\'e inequality and the integration by parts formula for Gaussian functionals, as well as classical tools from statistical asymptotic theory. Numerical evaluations validating the accuracy of the CLT confirm the asymptotic Gaussianity of the fluctuations of the SNR of the MVDR filter.Comment: This is a corrected version of the paper that will appear at IEEE Transactions on Signal Processing September 201

A pre-filtering maximum likelihood approach to multiple source direction estimation.

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