An Efficient Particle Filter for the OOSM Problem in Nonlinear Dynamic Systems
An Efficient Particle Filter for the OOSM Problem in Nonlinear Dynamic Systems
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DOI：
10.23919/icif.2018.8455401
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Ming Li;Wei Yi;Qi Yang;L. Kong
中科院分区：
文献类型：
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作者：
Ming Li;Wei Yi;Qi Yang;L. Kong
研究了非线性动态系统中任意滞后的失序测量问题。提出了一种基于精确贝叶斯解的粒子滤波算法.通常，通过引入一些合理的高斯假设，一个通用的高斯平滑器被导出来计算期望的平滑pdf，而不是使用粒子平滑器，这使得E-PF计算效率高，适用于大多数非线性情况。同时，对于E-PF，只存储预定最大滞后数的估计和协方差，也有效地节省了存储资源。仿真中给出了一个二维目标跟踪的例子，数值结果表明，该算法的跟踪性能与张等人提出的A-PF算法相当接近，而计算成本显著降低。
In this paper, the out of sequence measurement (OOSM) problem with arbitrary lags in nonlinear dynamic systems is considered. We develop an efficient particle filtering (E- PF) algorithm based on the exact Bayesian solution. Generally, by introducing some reasonable Gaussian assumptions, a general Gaussian smoother is derived to compute the expected smoothing pdfs instead of using the particle smoother, which makes the E-PF computation efficient and applicable for most nonlinear cases. Meantime, for E-PF, only the estimates and covariances for a predetermined maximum number of lags are stored, the storage resource is also effectively saved. In the simulation, a two-dimensional target tracking example is given, the numerical results show that the tracking performance of our algorithm is quite close to the A-PF algorithm proposed by Zhang et al., while the computation cost is significantly reduced.