Underdetermined direction of arrival estimation using acoustic vector sensor
This paper presents a new approach for the estimation of two-dimensional (2D) direction-of-arrival (DOA) of more sources than sensors using an Acoustic Vector Sensor (AVS). The approach is developed based on KhatriRao (KR) product by exploiting the subspace characteristics of the time variant covariance matrices of the uncorrelated quasi-stationary source signals. An AVS is used to measure both the acoustic pressure and pressure gradients in a complete sound field and the DOAs are determined in both horizontal and vertical planes. The identifiability of the presented KR-AVS approach is studied in both theoretic analysis and computer simulations. Computer simulations demonstrated that 2D DOAs of six speech sources are successfully estimated. Superior root mean square error (RMSE) is obtained using the new KR-AVS array approach compared to the other geometries of the non-uniform linear array, the 2D L-shape array, and the 2D triangular array.
direction of arrival estimation vector sensors microphone arrays covariance matrix