Distributed node-specific direction-of-arrival estimation in wireless acoustic sensor networks

Abstract

In this paper, we study the effect of collaboration between nodes for direction of arrival (DOA) estimation in a full connected wireless acoustic sensor network (WASN) where the position of the nodes is unknown. Each node is equipped with a linear microphone array which defines a node-specific DOA with respect to a single common target speech source. We assume that the DOA estimation algorithm is operated in conjunction with a distributed noise reduction algorithm, referred to as the distributed adaptive node-specific signal estimation (DANSE) algorithm. To avoid additional data exchange between the nodes, the goal is to exploit the shared signals used in the DANSE algorithm to also improve the node-specific DOA estimation. The DOA estimation is based on the multiple signal classification (MUSIC) algorithm (if sufficient computing power is available), or a least-squares (LS) method based on a locally estimated steering vector which allows to eliminate the exhaustive search in MUSIC and results in a significantly lower computational complexity. Simulation results demonstrate that collaboration between nodes improves the performance of the DOA estimation compared to the case where the nodes operate individually, i.e. do not collaborate.