Higher order statistics based Direction of Arrival estimation with single Acoustic Vector Sensor in the under-determined case

Abstract

Acoustic Vector Sensors (AVS) measure acoustic pressure as well as the acoustic particle velocity to estimate the acoustic intensity. The acoustic intensity, which is a vector quantity, represents the magnitude and direction of the active or propagating part of an acoustic field thus indicating the Direction of Arrival (DOA) of a received signal. This paper concerns with the feasibility of DOA estimation with a single acoustic vector sensor in the under-determined case where the number of sources are more than the number of sensors. It is proposed to use higher order statistics (cumulants) to increase the number of uniquely identifiable sources using a single vector sensor. The use of higher order statistics allows the increase in the dimension of the array steering matrix thereby resulting in increase of uniquely identifiable sources. The details of the proposed algorithm along with the number of sources which can be uniquely indentified are presented in this paper. It has been observed that a single AVS using fourth order statistics is capable of uniquely identifying eight sources with not more than four sources in a single plane. The number of sources can be increased by working with further higher order statistics of the order of 2q (for q ≥ 2). All the results of the proposed algorithm have been verified through suitable simulations.