DOA Estimation Based on Enhanced SRP-MVDR Using Kronecker Product Decomposition for Large Rectangular Microphone Arrays

Abstract

Direction-of-arrival (DOA) estimation is a key process in microphone array systems. The steered response power-based minimum variance distortionless response (SRP-MVDR) method performs very well in challenging acoustic environments but suffers from exponential complexity as the number of microphones increases. To improve the efficiency of SRP-MVDR for real-time applications, we propose a Kronecker product-based SRP-MVDR (SRP-KPMVDR) method designed for large rectangular microphone arrays. This approach begins with a rank-one approximation that represents the signal covariance matrix of a rectangular microphone array in Kronecker product form, which is essential for SRP-MVDR estimation. By utilizing the Kronecker product properties, the complex matrix inversion in SRP-MVDR is simplified to the inversion of two smaller matrices, significantly reducing computational complexity. Simulation results show that the SRP-KPMVDR method achieves comparable performance to the traditional SRP-MVDR while greatly decreasing the computational demands.