文章摘要
邹鲲,来磊,骆艳卜,李伟.样本缺失情况下的雷达目标自适应检测[J].空军工程大学学报:自然科学版,2020,21(6):73-78
样本缺失情况下的雷达目标自适应检测
Radar Target Adaptive Detection with Missing Samples
  
DOI:
中文关键词: 自适应检测  数据缺失  期望最大算法  马尔科夫链蒙特卡洛方法  Gibbs抽样
英文关键词: adaptive detection  missing observations  EM algorithm  MCMC method  Gibbs sampler
基金项目:国家自然科学基金(61871396); 博士后基金(2017M623352, 2018T111148);陕西省自然科学基金(2020JM343, 2020JM352)
作者单位
邹鲲,来磊,骆艳卜,李伟 空军工程大学信息与导航学院 西安 710077 
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中文摘要:
      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。
英文摘要:
      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.
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