KK分布杂波下的距离扩展目标检测算法

针对服从KK分布的大拖尾雷达杂波背景下的扩展目标检测问题,利用球不变随机变量表示了KK分布雷达杂波模型。在假设目标回波幅度已知的情况下,研究了基于Neyman-Pearson准则的距离扩展目标最优积累检测器,并通过对目标幅度的最大似然估计,推导了广义最大似然比检验检测器模型。为了降低这两种检测器中因计算第二类修正的贝塞尔函数而引入的运算复杂度,提出了一种基于顺序统计量的广义似然比检测器。该检测器利用检测窗内幅度较大的距离单元回波作为目标回波进行判决。利用蒙特卡罗仿真对这三种算法的性能进行了验证与比较,虽然最优积累检测器与广义似然比检测器具有更好的检测性能,但实现困难,计算量大,而基于顺序统计量的广义似然比检测器则具有更高的实用性。

Range-spread target detection in KK-distributed clutter

Aiming at the range-spread target detection in KK-distributed heavy-tailed radar clutter background, the KK-distributed clutter is taking as a spherically invariant random vector (SIRV), the Neyman-Pearson optimal integrator for the range-spread target detection with known target amplitude is derived firstly. Then by replacing the ideal target amplitude with the maximum likelihood estimates, the generalized likelihood ratio test (GLRT) is obtained. Both of the detectors are dependent on the modified Bessel function of the second kind, which makes the detectors computationally complicated. A suboptimal generalized likelihood ratio test based on order statistics (OS-GLRT) is proposed. The OS-GLRT makes use of some largest observations from the range cells occupied by the most likely target scatters. The performance assessment conducted by Monte Carlo simulation validates that the optimal integrator and GLRT has better performance, however, they are hard to applied, and the OS-GLRT is a more practical detector.