Frequency Invariant Beamforming for a SmallSized BiCone Acoustic Vectorxe2x80x93Sensor Array In this work, we design a smallsized bicone acoustic vectorsensor array (BCAVSA) and propose a frequency invariant beamforming method for the BCAVSA, inspired by the Ormia ochraceaxe2x80x99s coupling ears and harmonic nesting. First, we design a BCAVSA using several sets of cylindrical acoustic vectorsensor arrays (AVSAs), which are used as a guide to construct the constant beamwidth beamformer. Due to the mechanical coupling system of the Ormia ochraceaxe2x80x99s two ears, the phase and amplitude differences of acoustic signals at the bilateral tympanal membranes are magnified. To obtain a virtual BCAVSA with larger interelement distances, we then extend the coupling magnified system into the BCAVSA by deriving the expression of the coupling magnified matrix for the BCAVSA and providing the selecting method of coupled parameters for fitting the underwater signal frequency. Finally, the frequency invariant beamforming method is developed to acquire the constant beamwidth pattern in the threedimensional plane by deriving several sets of the frequency weighted coefficients for the different cylindrical AVSAs. Simulation results show that this method achieves a narrower mainlobe width compared to the original BCAVSA. This method has lower sidelobes and a narrower mainlobe width compared to the coupling magnified bicone pressure sensor array.

Sparse representation based directionofarrival estimation using circular acoustic vector sensor arrays Abstract   Directionofarrival (DOA) estimation using circular array has been attracted significant attention in passive sonar system. Recently, the DOA estimation techniques face some challenges such as the small angle spacing of the incident signals, high correlation or coherence between signals, the considerable ambient noise, etc. To solve these problems, this paper proposes a sparse representation (SR) algorithm to estimate the DOAs of signals in the isotropic ambient noise by using the circular arrays composed of acoustic vector sensors (CAVSA). By exploiting the correlation characteristic of the acoustic pressure and particle velocity, two crosscovariance matrices between the acoustic pressure and x, yaxes particle velocity are first constructed to eliminate the isotropic ambient noise. Then they are stacked to form an augmented crosscovariance matrix, which fully utilizes the direction information of the x and yaxes particle velocity components. It is observed an interesting fact that when the number of snapshots is limited, the augmented crosscovariance matrix can be divided into the autocorrelation terms of each signal and the crosscorrelation terms of any two incident signals coming from different directions. Based on this, the virtual overcomplete and the extra bases between the acoustic pressure and particle velocity are constructed by KhatriRao and Kronecker products, respectively. Finally, the SRbased DOA estimation algorithm is derived via the SR of the augmented crosscovariance vector. Simulation and experimental results demonstrate that the proposed method outperforms some existing DOA estimation methods in terms of the spatial spectrum, the estimation accuracy, and the angular resolution.