矢量水听器改进高分辨Eigenspace算法

Improved Eigenspace algorithm based on vector hydrophone

针对传统高分辨方位谱估计算法在水声低信噪比环境下性能较差,且对相干源的方位估计性能显著下降的问题,本文在Eigenspace算法的基础上,提出了基于声矢量阵声压振速联合处理的Eigenspace-Wiener算法和空间平滑的Eigenspace-Wiener算法。本文将常规Eigenspace算法的推广应用于声矢量阵,采用维纳后置滤波算法,可以有效降低旁瓣;针对相干源,进行空间平滑处理后采用维纳后置滤波算法,可以增强解相干能力且抑制噪声。计算机仿真结果表明:Eigenspace-Wiener算法较Eigenspace算法旁瓣更低;空间平滑Eigenspace-Wiener算法相对于空间平滑MUSIC算法具有良好的相干目标分辨性能。

The traditional high-resolution azimuth spectrum estimation algorithm performs poorly at a low signal-to-noise ratio,and the performance of coherent source estimation is also reduced.To solve this problem,we extend the regular Eigenspace algorithm and apply it to the acoustic vector array.To do so,we propose the Eigenspace-Wiener algorithm and the spatial smoothing Eigenspace-Wiener algorithm based on the joint processing of the sound pressure and vibration velocity of the acoustic vector array.Adopting a Wiener post-filtering algorithm effectively reduces the side lobes.For coherent sources,the use of Wiener post-filtering after spatial smoothing enhances the decoherence ability and suppresses noise.The computer simulation results show that the Eigenspace-Wiener algorithm has smaller side lobes than the Eigenspace algorithm and the spatial smoothing Eigenspace-Wiener algorithm has better coherent target resolution performance than the spatial smoothing MUSIC algorithm.