Low complexity gain-phase error correction for adaptive underdetermined DOA estimation in sensor arrays

Abstract

Direction of Arrival (DOA) estimation techniques are essential for determining the locations of signal sources using sensor arrays. For a uniform linear array (ULA), the number of detectable sources is limited to one less than the number of sensors. Sparse linear arrays (SLAs) overcome this limitation by leveraging the difference array to estimate more sources than sensors. However, gain and phase mismatches among sensors can impair accuracy. Existing algorithms to correct these mismatches are computationally demanding, making them unsuitable for low-power IoT devices. This paper proposes a novel method to integrate gain-phase compensation into adaptive filtering-based DOA estimation algorithms. The proposed approach reduces computational complexity and improves performance, especially in low SNR and low snapshot scenarios, facilitating efficient deployment in low-power devices.