Analysis of Partially-Calibrated Sparse Subarrays for Direction Finding with Extended Degrees of Freedom

TL;DR

This paper introduces the GCA-MUSIC algorithm for DOA estimation using partially-calibrated sparse subarrays, leveraging difference coarrays to improve source estimation beyond physical sensor limits.

Contribution

The paper proposes a novel GCA-MUSIC algorithm that exploits difference coarrays and a pseudo-spectrum merging rule for enhanced DOA estimation with partially-calibrated sparse arrays.

Findings

GCA-MUSIC outperforms existing methods in simulations.

The algorithm achieves higher degrees of freedom in source estimation.

Numerical results validate improved accuracy and robustness.

Abstract

This paper investigates the problem of direction-of-arrival (DOA) estimation using multiple partially-calibrated sparse subarrays. In particular, we present the Generalized Coarray Multiple Signal Classification (GCA-MUSIC) DOA estimation algorithm to scenarios with partially-calibrated sparse subarrays. The proposed GCA-MUSIC algorithm exploits the difference coarray for each subarray, followed by a specific pseudo-spectrum merging rule that is based on the intersection of the signal subspaces associated to each subarray. This rule assumes that there is no a priori knowledge about the cross-covariance between subarrays. In that way, only the second-order statistics of each subarray are used to estimate the directions with increased degrees of freedom, i.e., the estimation procedure preserves the coarray Multiple Signal Classification and sparse arrays properties to estimate more sources than the number of physical sensors in each subarray. Numerical simulations show that the proposed GCA-MUSIC has better performance than other similar strategies.