Distributed Adaptive Node-Specific Signal Estimation in a Wireless Sensor Network with Partial Prior Knowledge of the Desired Source Steering Vector

This paper first introduces the centralized generalized eigenvalue decomposition (GEVD) based multichannel Wiener filter (MWF) with prior knowledge for node-specific signal estimation in a wireless sensor network (WSN), where (some of) the nodes have partial prior knowledge of the desired source steering vector. A distributed adaptive estimation algorithm for a fully-connected WSN is then proposed demonstrating that this MWF can be obtained by letting the nodes work on compressed (i.e. reduced-dimensional) sensor signals compared to the centralized approach. The algorithm can be used in applications such as speech enhancement in an acoustic sensor network, where (some of) the nodes nodes have prior knowledge on the location of the desired speech source and on their local microphone array geometry or have access to clean noise reference signals.