Structured Total Least Squares Based Internal Delay Estimation For Distributed Microphone Auto-Localization

Abstract

Auto-localization in wireless acoustic sensor networks (WASNs) can be achieved by time-of-arrival (TOA) measurements between sensors and sources. Most existing approaches are centralized, and they require a fusion center to communicate with other nodes. In practice, WASN topologies are time-varying with nodes joining or leaving the network, which poses scalability issues for such algorithms. In particular, for an increasing number of nodes, the total transmission power required to reach the fusion center increases. Therefore, in order to facilitate scalability, we present a structured total least squares (STLS) based internal delay estimation for distributed microphone localization where the internal delay refers to the time taken for a source signal reaching a sensor to that it is registered as received by the capture device. Each node only needs to communicate with its neighbors instead of with a remote host, and they run an STLS algorithm locally to estimate local internal delays and positions (i.e., its own and those of its neighbors), such that the original centralized computation is divided into many subproblems. Experiments demonstrate that the decentralized internal delay estimation converges to the centralized results with increasing signal-to-noise ratio (SNR). More importantly, less computational complexity and transmission power are required to obtain comparable localization accuracy.