Particle filters for state estimation of jump Markov linear systems
Particle filters for state estimation of jump Markov linear systems
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DOI：
10.1109/78.905890
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发表时间：
2001-03-01
影响因子：
5.4
通讯作者：
Krishnamurthy, V
中科院分区：
文献类型：
--
作者：
Doucet, A;Gordon, NJ;Krishnamurthy, V
跳马尔可夫线性系统（JMLS）是一类参数按有限状态马尔可夫链随时间演化的线性系统，本文的目的是递归地计算这类系统的最优状态估计。我们提出了高效的基于模拟的算法，称为粒子滤波器来解决最优滤波问题以及最优固定滞后平滑问题。我们的算法结合联合收割机顺序重要性抽样。选择方案和马尔可夫链蒙特卡罗方法，它们采用了几种方差缩减方法，充分利用了JMLS的统计结构，并进行了计算机模拟，以评估所提出的算法的性能，考虑了在线反卷积脉冲过程和跟踪机动目标的问题。结果表明，我们的算法优于目前的方法。
Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time according to a finite state Markov chain, In this paper, our aim is to recursively compute optimal state estimates for this class of systems. We present efficient simulation-based algorithms called particle filters to solve the optimal filtering problem as well as the optimal fixed-lag smoothing problem. Our algorithms combine sequential importance sampling. a selection scheme, and Markov chain Monte Carlo methods, They use several variance reduction methods to make the most of the statistical structure of JMLS.Computer simulations are carried out to evaluate the performance of the proposed algorithms, The problems of on-line deconvolution of impulsive processes and of tracking a maneuvering target are considered. It is shown that our algorithms outperform the current methods.