基于分布式声矢量传感器MIMO声呐的OMP-DCD成像算法

OMP-DCD algorithm based on distributed MIMO sonar imaging with acoustic vector sensor

针对声压传感器条件下,声呐成像的左右模糊问题;针对常规MIMO声呐成像算法分辨力受瑞利限的限制问题;针对压缩感知算法重构信号中的数值不稳定问题,本文利用声矢量传感器接收到的信号振速信息和声压信息,利用目标在空间域分布的稀疏性,在压缩感知理论下,提出了基于声矢量传感器的二维坐标下降法(DCD)算法改进的正交匹配追踪(OMP)算法。仿真结果表明,提出的算法可以精准地估计出空间目标的方位;与传统的分布式MIMO成像算法反向投影(Back Projection,BP)相比,使用更少的实验数据,降低了运算的复杂度;当信噪比为10 d B的条件下,BP算法当主瓣级为0 d B时,在x,y,z轴最大的旁瓣级分别为–24.5 d B,–24.3 d B,–5.5 d B,而提出的算法可以获得一个稀疏解,且有效的避免左右模糊问题;避免重构信号中的矩阵求逆运算,数值不稳定得到了解决;并在信噪比为–5 d B以下时,定位精度高于声压OMP-DCD算法,具有更高的抗噪声能力和系统辨识能力。

As the left/right ambiguity problem of sonar imaging with scalar vector, and as the matrix inverse problem of compressed sensing signal reconstruction; also as traditional algorithms of sonar imaging need a large amount of data, in view of the above questions, with the information about vibration velocity and sound pressure of received signal by vector sensor, using the sparse distribution of the target in the space domain, under compressed sensing theory, a vector sensor sonar imaging algorithm based on two-dimensional coordinate descent method(DCD) algorithm improved orthogonal matching pursuit(OMP) algorithm is proposed. Simulation results show that the proposed algorithm can accurately estimate the space target location. Compared with traditional BP algorithm, the proposed algorithm using fewer data to reduce the computational complexity. Under the condition of SNR is 10 d B, Back Projection(BP) algorithm has a wider main lobe and high side lobe level, when the main lobe amplitude is 0 d B, the maximum side lobe amplitude at x, y, z axis is –24.5 d B, –24.3 d B, –5.5d B respectively. The proposed algorithm can avoid left/right ambiguity problem and get a sparse solution. The proposed algorithm can avoid the numerical instability problem in the process of matrix inversion. When SNR is below –5 d B, the proposed algorithm has higher positioning accuracy and higher ability of anti-noise ability and system identification than the scalar OMP-DCD algorithm.