Coherence Analysis using Canonical Coordinate Decomposition with Applications to Sparse Processing and Optimal Array Deployment

摘要

Sparse array processing methods are typically used to improve the spatial resolution of sensor arrays for the estimation of direction of arrival (DOA). The fundamental assumption behind these methods is that signals that are received by the sparse sensors (or a group of sensors) are coherent. However, coherence may vary significantly with the changes in environmental, terrain, and, operating conditions. In this paper canonical correlation analysis is used to study the variations in coherence between pairs of sub-arrays in a sparse array problem. The data set for this study is a subset of an acoustic signature data set, acquired from the US Army TACOM-ARDEC, Picatinny Arsenal, NJ. This data set is collected using three wagon-wheel type arrays with five microphones. The results show that in nominal operating conditions, i.e. no extreme wind noise or masking effects by trees, building, etc., the signals collected at different sensor arrays are indeed coherent even at distant node separation.