中文摘要：

该文针对在辅助数据有限的情况下自适应检测器出现检测性能损失，提出基于杂波的先验知识分布的距离扩展目标自适应检测算法。复合高斯杂波的纹理和散斑的协方差矩阵分别被建模为服从逆伽玛分布的随机变量和逆复 Wishart 分布的随机矩阵。利用先验知识推导了纹理分量的最大后验估计，并结合广义似然比检验设计了不依赖辅助数据的距离扩展目标自适应检测器。仿真结果表明，提出的检测器在参数失配条件下具有好的鲁棒性，而且在辅助数据有限情况下检测性能优于传统的广义似然比检测器的检测性能。

英文摘要：

For the problem of detection performance loss of adaptive detectors on the condition that the secondary data are limited, the adaptive detection method of range-spread target based on the prior knowledge of clutter is proposed. The texture and the covariance matrix of speckle of clutter are respectively modeled as the random variable which follows the inverse Gamma distribution and the random matrix which follows the inverse complex Wishart distribution. Based on the prior knowledge, the MaximumA Posteriori（MAP） estimation of texture component is obtained and the adaptive detector of range spread target which does not need the secondary data is designed via utilizing the generalized likelihood ratio test. Finally, the detection performances of the proposed detector are evaluated and the experimental results illustrate that the proposed detector is robust in parameters mismatched situation and outperforms the conventional generalized likelihood ratio test detector for range-spread target in limited secondary data scenarios.