Minimum Mean-Squared-Error Autocorrelation Processing in Coprime Arrays

TL;DR

This paper introduces a novel MMSE-based autocorrelation estimation method for coprime arrays, significantly improving DoA estimation accuracy over traditional suboptimal techniques.

Contribution

It develops a minimum-MSE autocorrelation estimator for coprime arrays, optimizing DoA estimation performance for any source distribution.

Findings

Superior autocorrelation estimates with MMSE lead to better DoA accuracy

Outperforms traditional selection and averaging methods

Numerical results confirm enhanced estimation performance

Abstract

Coprime arrays enable Direction-of-Arrival (DoA) estimation of an increased number of sources. To that end, the receiver estimates the autocorrelation matrix of a larger virtual uniform linear array (coarray), by applying selection or averaging to the physical array's autocorrelation estimates, followed by spatial-smoothing. Both selection and averaging have been designed under no optimality criterion and attain arbitrary (suboptimal) Mean-Squared-Error (MSE) estimation performance. In this work, we design a novel coprime array receiver that estimates the coarray autocorrelations with Minimum-MSE (MMSE), for any probability distribution of the source DoAs. Our extensive numerical evaluation illustrates that the proposed MMSE approach returns superior autocorrelation estimates which, in turn, enable higher DoA estimation performance compared to standard counterparts.