样本缺失情况下的雷达目标自适应检测

雷达信号自适应检测问题中，参考数据中部分样本丢失会导致常规检测器性能显著下降。在无先验信息条件下，采用期望最大算法获得杂波协方差矩阵的最大似然估计，得到基于期望最大算法的自适应匹配滤波器。利用探测环境的先验信息，在贝叶斯框架下，采用Gibbs抽样获得杂波协方差矩阵的后验均值估计，得到基于马尔科夫链蒙特卡洛自适应匹配滤波器。计算机仿真分析表明，这2种检测器可以在样本缺失情况下具有较好的检测性能。当杂波协方差矩阵先验信息较少时，EM-AMF与MCMC-AMF检测性能相当；当有先验信息可供利用时，MCMC-AMF的检测性能可以得到进一步提升。

Radar Target Adaptive Detection with Missing Samples

In radar target adaptive detection problems, parts of the samples in the reference data are missing, such case is as a result of the degradation of detection performance for conventional detectors. Being under no condition of a prior information, an expectation maximization based adaptive matched filter (EM-AMF) is obtained by the maximal likelihood estimate of the clutter covariance matrix. A prior information about the detection environment is utilized for obtaining Markov Chain Monte Carlo based adaptive matched filter (MCMC-AMF) under condition of using Gibbs sampler to obtain the posterior mean of the clutter covariance matrix. The computer simulation results show that both of the detectors perform well under condition of the missing samples. With less prior information, MCMC-AMF and EM-AMF possess the analogous detection performance. But the MCMC-AMF detection performance can be improved further by exploiting more prior information.