IEEE Transactions on Signal Processing
Acoustic vector-sensor array processing
Acoustic vector-sensor array processing
On the virtual array concept for the fourth-order direction findingproblem
IEEE Transactions on Signal Processing
Identifiability in array processing models with vector-sensorapplications
IEEE Transactions on Signal Processing
Direction finding algorithms based on high-order statistics
IEEE Transactions on Signal Processing
Near-field/far-field azimuth and elevation angle estimation using asingle vector hydrophone
IEEE Transactions on Signal Processing
Acoustic vector-sensor array processing
IEEE Transactions on Signal Processing
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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 Khatri-Rao (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.