DOA estimation of closely-spaced and spectrally-overlapped sources using a STFT-based MUSIC algorithm

• A short-time Fourier transform (STFT) based MUSIC algorithm is proposed.
• The proposed STFT-MUSIC algorithm has a low implementation complexity.
• A selection method of single-source time-frequency (TF) points in case of complex-valued mixing matrix is proposed.
• The STFT-MUSIC algorithm can be implemented with a small number of sensors in underdetermined cases.
• The STFT-MUSIC algorithm is especially suitable for closely-spaced and spectrally-overlapped sources.

The multiple signal classification (MUSIC) algorithm based on spatial time-frequency distribution (STFD) has been investigated for direction of arrival (DOA) estimation of closely-spaced sources. However, the limitations of the bilinear time-frequency based MUSIC (TF-MUSIC) algorithm lie in that it suffers from heavy implementation complexity, and its performance strongly depends on appropriate selection of auto-term location of the sources in time-frequency (TF) domain for the formulation of a group of STFD matrices, which is practically difficult especially when the sources are spectrally-overlapped. In order to relax these limitations, this paper aims to develop a novel DOA estimation algorithm. Specifically, we build a MUSIC algorithm based on spatial short-time Fourier transform (STFT), which effectively reduces implementation cost. More importantly, we propose an efficient method to precisely select single-source auto-term location for constructing the STFD matrices of each source. In addition to low complexity, the main advantage of the proposed STFT-MUSIC algorithm compared to some existing ones is that it can better deal with closely-spaced sources whose spectral contents are highly overlapped in TF domain.