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胡 睿, 孙进平, 王文光(北京航空航天大学电子信息工程学院, 北京 100083)

摘 要
在SAR图像目标检测中,分布模型与杂波的拟合精度对基于统计模型的CFAR(constant false alarm rate)检测算法性能有着重要的影响。在极不均匀区域,由于存在着大量的强脉冲干扰,使得常用的分布模型的拟合精度都有所下降。基于广义中心极限定理的α稳定分布能对强脉冲干扰现象准确地建模,对各种性质区域的杂波都有较好的适应性。本文对基于α稳定分布的SAR图像目标CFAR检测算法进行了研究,给出了参数估计、标准模型变换及检测阈值确定等关键步骤的实现方法。对实际数据的处理表明,该算法具有较好的检测性能,能达到较高的检测率和较低的虚警率。
Target Detection of SAR Images Using Alpha Stable Distribution

HU Rui, SUN Jinping, WANG Wenguang(School of Electronics and Information Engineering, Beihang University, Beijing 100083)

In SAR images, the goodness-of-fit of distribution model to SAR clutter data has great effect on the performance of statistical based CFAR (constant false alarm rate) target detection algorithms. In extreme nonhomogeneous region, the common distribution models can not describe the clutter data accurately due to strong impulsive interference. Whereas, α stable distribution which is based on Generalized Central Limit theorem can model the strong impulsive interference accurately and is fit for both homogeneous and nonhomogeneous clutter regions. So a CFAR detection algorithm using α stable distribution is studied in this paper and some key steps, such as parameter estimating, model standardization and threshold calculating is also included. The results on real data show that the SαS CFAR has good performance and can achieve high detection rate and low false alarm rate.