EM算法在杂波环境下机动目标跟踪中的应用研究
Study of Application EM Algorithm on Tracking Maneuvering Targets with Clutter
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摘要: EM(Expectation-Maximization)作为一种迭代求解非完备数据条件下极大似然(后验)参数估计问题的方法,在目标跟踪领域主要应用于被动跟踪及实时性要求不高的目标环境.该文推广了L.A.Johnston的理论成果,推导得出了一种基于AECM(Alternative Expectation ConditionMaximization)方法的杂波环境下实时机动目标跟踪箅法,算法中后验模型概率与关联概率由隐马尔科夫模型滤波计算得到.仿真计算表明,所提算法跟踪精度与IMM-PDA性能相当,算法是有效的.Abstract: The EM algorithm, as an iterative numerical tool for computing maximum likelihood (or MAP) parameter estimates for incomplete data problem, has been used in area of target tracking, particularly in passive tracking and scenario in which real-time processing is unnecessary. As an extension of .Johnstons recent work, a recursive algorithm for tracking maneuvering targets in clutter, which based on AECM algorithm, is developed in this paper. In this algoritlim, model posterior probability and data association probability are computed via HMM filter respectively. Computer simulation indicates that performance of the algorithm is comparable with that of IMM-PDA, and the algorithm is valid.
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