High-Resolution DOA Estimation Algorithm for a Single Acoustic Vector Sensor at Low SNR

In order to improve the angular resolution of direction-of-arrival (DOA) estimation based on portable microphone at low signal-noise-ratio (SNR), this paper proposes a new high-resolution DOA estimation algorithm based on single acoustic vector sensor (AVS), namely Virtual-AVS algorithm. The innovative algorithm is mainly composed of two parts: the preprocessing method and the DOA estimation method with ambiguity elimination. The preprocessing method is implemented based on higher-order cumulants with Gaussian noise suppression characteristic. This method not only suppress Gaussian noise to effectively enhance SNR, but also achieve AVS virtual expansion. The computational burden can be significantly reduced by effectively expanding one necessary AVS to perform the ESPRIT algorithm. To extend the application range of the innovative algorithm to support non-point-like geometry AVS, we introduce a new DOA estimation method with ambiguity elimination. This method can eliminate the cyclic ambiguity caused by the channel spacing of the non-point-like geometry AVS exceeding the half-wavelength of the incident source. We prove that the Virtual-AVS algorithm is asymptotically unbiased with large number of snapshots. We also derive the Cram$\mathbf {\acute{e}}$r-Rao bound. The simulations of closely-spaced sound sources show that the algorithm has better angular resolution than the existing algorithms. The real-world experiments of semi-anechoic chamber also verify the feasibility of the algorithm.