DOA and polarization estimation for compressed EMVS array with arbitrary sensor geometry

Abstract

Electromagnetic vector sensors (EMVS) are widely used in array signal processing because of their advantages in polarization diversity, which enables the estimation of the received polarization angle (RPA) in addition to the direction of arrival (DOA). However, these benefits are accompanied by a huge burden on hardware cost and computational complexity. Specifically, compared to scalar arrays, EMVS array signal processing requires more complex hardware devices, for instance, a larger number of front-end chains and higher computational complexity for multidimensional parameter estimation algorithms. We propose a novel compression framework to reduce the hardware cost while achieving high performance. Unlike the existing ones in the literature, the proposed compression network is a sparse network consisting of analog phase shifters; In addition, the proposed compression framework only works on spatial information except for polarization information; Further, for such a compressed EMVS array, we propose a two-step multi-parameter estimation algorithm. In detail, we firstly propose a compressed ESPRIT-like method to estimate the 2D-DOA coarsely. Then, we adopt a small-scale 2D search in the vicinity of the coarse estimate to obtain a high-accuracy estimate of 2D-DOA. After that, we obtain a high-accuracy estimate of 2D-RPA in a closed-form. Furthermore, to avoid any possible performance deterioration of the proposed estimators due to the randomly selected compression weights (a.k.a. compression matrix) from Gaussian distribution (as in literature) and thus cause loss of information, we propose an optimization algorithm to find the optimal solution for the compression matrix under the maximum signal-to-noise ratio (SNR) principle. In this way, we convert the intractable sparse complex matrix optimization problem into a quadratically constrained quadratic programming (QCQP); Then, we employ the semidefinite relaxation technique (SDR) to find the optimal compression matrix. Numerical simulations illustrate that the proposed algorithms could obtain high-accuracy multi-parameter estimates with reduced front-end chains and low computational complexity.