态势估计中一种基于贝叶斯估计的统计时间推理方法

统计时间推理是态势估计中的一个重要组成部分。Ｋｉｒｉｌｏｖ的基于极大似然估计（Ｍａｘｉ－ｍｕｍＬｉｋｅｌｉｈｏｏｄＥｓｔｉｍａｔｉｏｎ，ＭＬＥ）的推理方法将未知时间变量看作常数，估计方差较大。文中建立的已知时间信息和未知时间变量之间的关系模型，将未知时间变量扩展为随机变量，将贝叶斯估计（ＢａｙｅｓＥｓｔｉｍａｔｉｏｎ，ＢＥ）引入时间推理。经过对两种推理算法的性能进行分析和比较，发现在一定范围内，基于ＢＥ的方法性能优于基于ＭＬＥ的方法。

A Stochastic Temporal Reasoning Method Based on Bayes Estimation in Situation Assessment

Stochastic temporal reasoning in situation assessment (SA)is very important.The approach based on Maximum Likeihood (MLE)treats the unknown temporal variable as a constant,which generates a larger estimate variance,In this paper,the relation model of known temporal information and unknown temporal variable has been established.The reasoning algorithm about time instants has been derived by treating the unknown temporal variable as random variable and introducing Bayes Estimation (BE)into temporal reasoning.The results show that under some conditions,the estimate variance of BE-based method is lower.