联邦式扩展卡尔曼粒子滤波算法

宁小磊; 李文博; 范斌

doi:10.15892/j.cnki.djzdxb.2011.02.003

2011 , Vol. 31 >Issue 2: 189 - 191,198

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2011.02.003

摘要

为了使联邦滤波器能有效处理非高斯、非线性系统的状态估计问题,提出将扩展卡尔曼粒子滤波引入联邦滤波结构中,得到一种新的联邦式扩展卡尔曼粒子滤波算法。使用扩展卡尔曼粒子滤波对联邦滤波子系统的多源数据进行处理,从而摆脱了经典卡尔曼滤波的限制,拓宽了联邦滤波器的实际应用范围。将联邦式扩展卡尔曼粒子滤波算法应用于非线性滤波器的一个标准验证模型进行了仿真实验,结果表明该算法是有效性的。

关键词： 信息融合; 联邦滤波; 粒子滤波; 非高斯; 非线性

本文引用格式

宁小磊 , 李文博 , 范斌 . 联邦式扩展卡尔曼粒子滤波算法[J]. 弹箭与制导学报, 2011 , 31(2) : 189 -191,198 . DOI: 10.15892/j.cnki.djzdxb.2011.02.003

Abstract

A new particle filter (federal extended Kalman particle filter, EKF-FPF) was proposed to estimate the state of non-Gaussian and non-linear system for federal filter, in which extended Kalman particle filer was introduced to federal filter so that the information fusion of subsystem can be solved by the non-Gaussian and non-linear filer. By doing so, the federal filter can get rid of the disadvantage of the ordinary Kalman filter to extend its application field. The simulation results of the standard testing model demonstrate the feasibility of the proposed algorithm.

Key words： information fusion; federal filter; particle filter; non-Gaussian; non-linear

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参考文献

[1]	Kerr T. Decentralized filtering and redundancy management for multisensor navigation [J]. IEEE Transactions on Aerospace and Electronic Systems, 1987, AES-23(1): 83-119.
[2]	Carlson N A. Federated filter for fault-tolerant integrated navigation system[C]// Proceedings of Position Location and Navigation Symposium, DLANS, 1988.
[3]	Carlson N A. Federated filter for fault tolerant integrated navigation systems[C]// IEEE PLANS 88: 110-119.
[4]	张明源, 王宏力. 强跟踪联邦的卡尔曼滤波器设计 [J]. 自动测量与控制, 2007, 26(6): 70-74.
[5]	Doucet A, Gordon N J, Krishnamurthy V. Particle filters for state estimation of jump Markov linear systems [J]. IEEE Trans. on Signal Processing, 2001, 49(5): 613-624.
[6]	De Freitas. Sequential Monte Carlo methods to train neural network models [J]. Neural Computation, 2000, 12(4): 955-993.
[7]	郭文艳, 韩崇昭, 雷明. 迭代无迹 Kalman 粒子滤波的建议分布 [J]. 清华大学学报(自然科学版), 2007, 47(2): 1866-1869.
[8]	胡昌华, 张琪, 乔玉坤. 强跟踪粒子滤波算法及其在故障预报中的应用 [J]. 自动化学报, 2008, 34(12): 1522-1528.
[9]	杜正聪, 唐斌, 李可. 混合退火粒子滤波器 [J]. 物理学报, 2006, 55(3): 999-1004.
[10]	韩崇昭, 朱洪艳, 段战胜. 多源信息融合 [M]. 北京: 清华大学出版社, 2006.

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