前视成像声呐最大熵超分辨算法及验证

声呐方位向上的接收信息可以看作是目标散射信息和波束方向图卷积的结果，通过解卷积的方法可以恢复出目标的散射信息，但是反问题的求解存在着固有的“病态性”问题。为了使解卷积的“病态性”问题转换成“良性”问题求解，文章采用最大熵作为正则化约束项，不局限于原始场景中的目标分布，利用Frieden熵作为衡量最终解的标准，引入图像熵对成像效果进行评价。结果表明，与限制迭代解卷积算法相比，该算法在低信噪比的条件下仍能表现出良好的超分辨性能。

Maximum entropy super-resolution algorithm of forward-looking image sonar and its verification

The received information of image sonar in azimuth can be regarded as the convolution result of scattering information with beam pattern, so the scattering information can be recovered by deconvolution. However, there is inherent ill-conditioned trouble in the solution of inverse problem. To solve the problem, the maximum entropy is introduced in this paper to use as the regularization constraint term, which is not limited to the distribution model of the targets, and the Frieden entropy is used to evaluate the final solution. Meanwhile, the image entropy is used to compare the results obtained by different algorithms. The results show that this algorithm has the good performance even in the low signal to noise ratio (SNR) compared with the constrained iterative deconvolution.