基于稀疏重构的前视声纳成像方法

Front-view Sonar Imaging Method Based on Sparse Reconstruction

基于稀疏重构的DOA估计算法可以通过加强表示稀疏性而获得更高分辨的空间谱估计,有助于实现相邻目标的区分,本文提出一种在每个距离上稀疏重构的声纳成像方法。该方法利用声纳成像中目标本身具有的稀疏性,以及稀疏重构算法中的范数约束,来获得更高的分辨率以最终实现成像效果的改善。在仿真和水池实验中,将l1-SVD和SpSF稀疏重构算法与传统方位估计方法MUSIC、CBF、SFW-L21、NN-SpSF进行性能对比,实验结果表明l1-SVD算法和SpSF算法成像优于传统方法,有较窄的主瓣和较低的旁瓣,且对背景噪声有一定的抑制效果。同时,对2个相邻很近的目标,也可较好地区分出来,表明本文算法具有较高的分辨率。

The DOA estimation algorithm based on sparse reconstruction can obtain higher-resolution spatial spectrum estimation by strengthening the sparsity of the representation,which is helpful to realize the differentiation of adjacent targets,and a sonar imaging method with sparse reconstruction at each distance is proposed.This method uses the sparsity of the target itself in sonar imaging and the norm constraint in the sparse reconstruction algorithm to obtain higher resolution and ultimately achieve the improvement of imaging effect.In the simulation and pool experiments,the performance of l1-SVD and SpSF sparse reconstruction algorithms is compared with the traditional azimuth estimation methods MUSIC,CBF,SFW-L21 and NN-SpSF,and the experimental results show that the l1-SVD algorithm and SpSF algorithm are better than the traditional methods,with narrower main lobes and lower side lobes,and have a certain suppression effect on background noise.At the same time,two targets that are close to each other can also be well distinguished,indicating a higher resolution.