Blind Direction-of-Arrival Estimation in Acoustic Vector-Sensor Arrays via Tensor Decomposition and Kullback-Leibler Divergence Covariance Fitting

Abstract

A blind Direction-of-Arrivals (DOAs) estimate of narrowband signals for Acoustic Vector-Sensor (AVS) arrays is proposed. Building upon the special structure of the signal measured by an AVS, we show that the covariance matrix of all the received signals from the array admits a natural lowrank 4-way tensor representation. Thus, rather than estimating the DOAs directly from the raw data, our estimate arises from the unique parametric Canonical Polyadic Decomposition (CPD) of the observations' Second-Order Statistics (SOSs) tensor. By exploiting results from fundamental statistics and the recently re-emerging tensor theory, we derive a consistent blind CPDbased DOAs estimate without prior assumptions on the array configuration. We show that this estimate is a solution to an equivalent approximate joint diagonalization problem, and propose an ad-hoc iterative solution. Additionally, we derive the Cramér-Rao lower bound for Gaussian signals, and use it to derive the iterative Fisher scoring algorithm for the computation of the Maximum Likelihood Estimate (MLE) in this particular signal model. We then show that the MLE for the Gaussian model can in fact be used to obtain improved DOAs estimates for non-Gaussian signals as well (under mild conditions), which are optimal under the Kullback-Leibler divergence covariance fitting criterion, harnessing additional information encapsulated in the SOSs. Our analytical results are corroborated by simulation experiments in various scenarios, which also demonstrate the considerable improved accuracy w.r.t. Zhang et al.'s state-of-theart blind estimate [1] for AVS arrays, reducing the resulting root mean squared error by up to more than an order of magnitude.

Enhanced Blind Calibration of Uniform Linear Arrays with One-Bit Quantization by Kullback-Leibler Divergence Covariance Fitting

“…Moreover, it can be easily shown that minimization of ( 9) is equivalent to maximization of the likelihood function for CN-distributed measurements (e.g., [22], Eq. ( 60)), which, in turn, is asymptotically equivalent to minimization of the optimally weighted nonlinear Least Squares (LS) objective function (see [22], Eq. ( 62), and Appendix E therein for the full details).…”