基于代价参考粒子滤波的存在概率检测算法

摘要

To improve the capability of detecting weak moving target with unknown statistical feature,a kind of track-before-detect (TBD) algorithm implemented by cost-reference particle filter (CRPF) is proposed (CRPF-TBD).Firstly,a discrete variable mimicking the existence and absence of a target is added to the state vector of CRPF.Particularly,besides transform probability matrix,the transformation of the discrete variables in the CRPF-TBD also depends on the correlative coefficient of successive measurements.Secondly,the existence probability at each frame is calculated from the output discrete variables.Finally,a binary test statistic is constructed from the existence probabilities.Target is declared to be present if the test statistic exceeds a given threshold.Simulation experiments of over-the-horizon radar (OTHR) target detection show that,in the case of given statistical feature,the detection performance of the proposed method is comparable to that of the traditional particle filter (PF) based likelihood ratio algorithm and existence probability detection method,and improved by more than 2dB when the statistical feature is unknown.The proposed method could be applied to surveillance in complex environment,such as radar and sonar.%为提高统计特性未知情况下对非线性微弱动目标的检测能力,本文提出一种基于代价参考粒子滤波的检测前跟踪算法.首先在代价参考粒子滤波的状态向量中增加模拟目标存在状态的离散变量,并在离散变量的转移过程中引入相关系数判决机制;其次,利用代价参考粒子滤波的输出估计存在概率;最后,基于存在概率构造检验统计量.当检验统计量大于给定门限时宣布目标出现.天波雷达目标检测的仿真表明,当系统的统计特性已知时,该方法的检测性能与基于传统粒子滤波的似然比检测、存在概率检测等相当;当统计特性未知时,该方法的检测性能比传统方法提高了2dB以上.本方法可用于复杂背景下的监测系统,如雷达、声呐等.