Robust Multi-Source Localization Over Planar Arrays Using MUSIC-Group Delay Spectrum

Abstract

Subspace-based source localization methods utilize the spectral magnitude of the MUltiple SIgnal Classification (MUSIC) method. However, in all these methods, a large number of sensors are required to resolve closely spaced sources. A novel method for high resolution source localization based on the group delay of MUSIC is described in this work. The method can resolve both the azimuth and elevation angles of closely spaced sources using a minimal number of sensors over a planar array. At the direction of arrival (DOA) of the desired source, a transition is observed in the phase spectrum of MUSIC. The negative differential of the phase spectrum also called group delay, results in a peak at the DOA. The proposed MUSIC-Group delay spectrum defined as product of MUSIC-Magnitude (MM) and group delay spectra, resolves spatially close sources even under reverberation owing to its spatial additive property. This is illustrated by performing spectral analysis of the MUSIC-Group delay function under reverberant environments. A mathematical proof for the spatial additive property of group delay spectrum is also provided. Source localization error analysis, sensor perturbation analysis, and Cramér-Rao bound (CRB) analysis are then performed to verify the robustness of the MUSIC-Group delay method. Experiments on speech enhancement and distant speech recognition are also conducted on spatialized TIMIT and MONC databases. Experimental results obtained using objective performance measures and word error rates (WER) indicate reasonable robustness when compared to conventional source localization methods in literature.