基于卷积构型的单元平均CFAR目标检测算法

提出一种基于卷积构型的单元平均恒虚警率(convolution based cell averaging constant false alarm rate, CCA-CFAR)快速检测算法.该算法首先根据背景杂波分布模型计算待检测合成孔径雷达(synthetic aperture radar, SAR)图像统计量矩阵, 然后对单元平均恒虚警率(cell averaging constant false alarm rate, CA-CFAR)检测器构建卷积模型, 利用卷积运算实现对背景杂波的矩估计, 并求出详细的背景杂波分布函数, 最后根据分布函数计算出每个像素的判定阈值, 并对所有待检测像素是否为目标点进行判定.该检测算法复杂度低, 运算效率高, 能够快速实现SAR图像实时目标检测.仿真实验证明了该方法的有效性和工程实用价值.

A cell averaging CFAR detector based on convolution for target detection in SAR images

A fast convolution based cell averaging constant false alarm rate (CCA-CFAR) detector based on convolution for target detection in synthetic aperture radar (SAR) images is proposed in this paper. As a first step, the statistic matrices of the SAR image are computed according to the background clutter distribution model. Then, a convolution model is built for the CA-CFAR detector to realize the moment estimation, and background clutter distribution function of all pixels can be obtained. Finally, the detect threshold for each pixel is calculated to determine whether the pixel is the target point. The algorithm has the advantages of low complexity and high computational efficiency, and can achieve SAR image real-time target detection. Experimental results demonstrate the effectiveness and usefulness of the proposed algorithm.