基于交互式多模型粒子滤波的状态估计方法

肖阳辉; 史泽林; 赵永廷

doi:10.15892/j.cnki.djzdxb.2011.06.012

2011 , Vol. 31 >Issue 6: 176 - 178

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2011.06.012

摘要

为了提高目标跟踪中状态估计环节的性能,基于交互式多模型粒子滤波的状态估计方法,采用交互式多模型(IMM)描述目标的运动过程,利用粒子滤波算法进行目标状态估计。方法避免了单一运动模型所带来的估计误差,同时克服了卡尔曼跟踪滤波算法的局限性,有效的提升了状态估计精确度。仿真实验证明了该方法在缺乏关于先验知识的情况下,对于不同的运动形式,均取得了较好的自适应性与鲁棒性。

关键词： 状态估计; 粒子滤波; IMM

本文引用格式

肖阳辉 , 史泽林 , 赵永廷 . 基于交互式多模型粒子滤波的状态估计方法[J]. 弹箭与制导学报, 2011 , 31(6) : 176 -178 . DOI: 10.15892/j.cnki.djzdxb.2011.06.012

Abstract

In order to improve the state estimation performance which belongs to target tracking, the interactive multiple model particle filter (IMMPF) adopts IMM to describe target movement, and estimate the target state by particle filters. IMMPF can avoid estimation error caused by single motion model. It also overcomes the limitation of Kalman tracking filter. When the priori knowledge is inadequate, IMMPF can estimate target position, velocity and acceleration with appropriate error under different form of exercise. So IMMPF improves adaptability and robustness.

Key words： state estimation; particle filter; IMM

参考文献

[1]	Arulampalam MS Maskell S Gordon N et al A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J]. IEEE Transactions on Signal Processing, 2002. 50(2): 174-188.
[2]	LIXR JILKOV V P A survey of maneuvering target tracking: Dynamic models[C]// Signal and Data Processing of Small Targets, SPIE Proc, 2000, vol. 4048: 212-235.
[3]	彭可茂申功璋文传源. 机动目标状态估计器算法研究[J]. 北京航空航天大学学报, 2004, 30(3): 276-279.
[4]	Mazor E Averbuch A Bar-Shalom Y Dayan J Interacting multiple model methods in target tracking: A survey[J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(1): 103-123.

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