严重拖尾复合高斯杂波中目标的自适应极化检测

Adaptive Polarimetric Detection of Targets in Heavy-tailed Compound-Gaussian Clutter

该文研究极化高分辨雷达在动态变化的杂波场景中自适应检测小目标的问题。将统计特性严重拖尾的杂波建模为纹理分量为逆伽马分布的复合高斯过程,借助于广义似然比检验和辅助数据得到了自适应极化检测器,并推导了该检测器的虚警概率表达式,证明了该检测器对协方差矩阵结构具有恒虚警特性。最后,利用仿真杂波数据验证了检测器检测性能的有效性。

The problem of detecting a weak target in dynamic clutter scenarios is analyzed with a polarimetric high-resolution radar. The heavy-tailed clutter is modeled by the compound-Gaussian process with inverse Gamma distributed texture. With training data to estimate covariance matrix of clutter, an adaptive polarimetric detector based on generalized likelihood ratio test criterion is presented for this heavy-tailed compound-Gaussian clutter. Then, the analytic expression of false alarm is derived to prove its constant false alarm rate property with respect to the clutter covariance matrix. The simulation results confirm the effectiveness of the proposed detector.