Direction of Arrival Estimation with Sparse Subarrays

TL;DR

This paper introduces new array architectures and algorithms for direction-of-arrival estimation using sparse subarrays, enabling the estimation of more sources than sensors with practical complexity.

Contribution

It proposes novel partially-calibrated array designs and DOA algorithms that outperform existing methods in source estimation capacity and computational efficiency.

Findings

Algorithms estimate more sources than sensors.

Proposed array configurations improve degrees of freedom.

Simulations show superior performance over existing approaches.

Abstract

This paper proposes design techniques for partially-calibrated sparse linear subarrays and algorithms to perform direction-of-arrival (DOA) estimation. First, we introduce array architectures that incorporate two distinct array categories, namely type-I and type-II arrays. The former breaks down a known sparse linear geometry into as many pieces as we need, and the latter employs each subarray such as it fits a preplanned sparse linear geometry. Moreover, we devise two Direction of Arrival (DOA) estimation algorithms that are suitable for partially-calibrated array scenarios within the coarray domain. The algorithms are capable of estimating a greater number of sources than the number of available physical sensors, while maintaining the hardware and computational complexity within practical limits for real-time implementation. To this end, we exploit the intersection of projections onto affine spaces by devising the Generalized Coarray Multiple Signal Classification (GCA-MUSIC) in conjunction with the estimation of a refined projection matrix related to the noise subspace, as proposed in the GCA root-MUSIC algorithm. An analysis is performed for the devised subarray configurations in terms of degrees of freedom, as well as the computation of the Cram\`er-Rao Lower Bound for the utilized data model, in order to demonstrate the good performance of the proposed methods. Simulations assess the performance of the proposed design methods and algorithms against existing approaches.