The authors introduce a constant false alarm rate (CFAR) detection algorithm, called K-CFAR, for automatic detection of underwater objects in sonar imagery. The K-CFAR adopts the K -distribution as a statistical model of the background. An efficient closed-form estimator for the K -distribution parameters is derived by the second-order approximation of the Polygamma function without involving a numerical iterative solution. A closed-form expression for the CFAR detection threshold is obtained by exploiting the first-order Laguerre approximation of the K -distribution. Then, to increase the probability of detection, a non-CFAR refinement to the K-CFAR, based on the spatial feature of the objects, is proposed. Experimental results obtained from 270 real sonar images of diverse environments demonstrate the superiority of the proposed algorithm compared to the state-of-the-art in terms of receiver operating characteristic curves.

中文翻译：

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声纳图像中目标的CFAR检测算法

作者介绍了一种恒定误报率（CFAR）检测算法，称为K-CFAR，用于自动检测声纳图像中的水下物体。K-CFAR采用ķ -分布作为背景的统计模型。一个有效的闭式估计器ķ -分布参数是通过Polygamma函数的二阶近似导出的，而不涉及数值迭代解。CFAR检测阈值的封闭式表达式是通过利用的一阶Laguerre近似获得的。ķ -分配。然后，为了增加检测的可能性，提出了基于对象的空间特征的对K-CFAR的非CFAR改进。从不同环境的270张真实声纳图像获得的实验结果证明，与接收机工作特性曲线相比，该算法与最新技术相比具有优越性。