Direction of Arrival Estimation with Sparse Subarrays
W. Leite, R. C. de Lamare, Y. Zakharov, W. Liu, M. Haardt
TL;DR
This work tackles DOA estimation in underdetermined scenarios using partially-calibrated sparse subarrays by introducing Type-I and Type-II array designs and two coarray-domain estimators, GCA-MUSIC and GCA-rMUSIC. The methods exploit spatial smoothing and affine-projection techniques across subarrays to recover multiple sources, potentially exceeding the number of sensors, while maintaining practical complexity. The authors provide a comprehensive analysis of degrees of freedom, derive a CRLB for the partially-calibrated model, and demonstrate through extensive simulations that the proposed approaches achieve high accuracy with favorable computational efficiency compared to existing methods. The results suggest significant practical benefits for sensor array processing in radar, communications, and related domains, especially when hardware constraints limit full calibration or dense sensing.
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 Cramèr-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.