Interacting multiple model particle filter

被引:170
|
作者
Boers, Y [1 ]
Driessen, JN [1 ]
机构
[1] Thales Nederland, NL-7554 RR Hangelo, Netherlands
关键词
D O I
10.1049/ip-rsn:20030741
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
A new method for multiple model particle filtering for Markovian switching systems is presented. This new method is a combination of the interacting multiple model (IMM) filter and a (regularised) particle filter. The mixing and interaction is similar to that in a conventional IMM filter. However, in every mode a regularised particle filter is running. The regularised particle filter probability density is a mixture of Gaussian probability densities. The proposed method is able to deal with nonlinearities and non-Gaussian noise. Furthermore, the new method keeps a fixed number of particles in each mode, and therefore it does not suffer from the potential drawbacks of existing multiple model particle filters for Markovian switching systems.
引用
收藏
页码:344 / 349
页数:6
相关论文
共 50 条
  • [1] Interacting Multiple Model Gaussian Particle Filter
    Liu, Zhigang
    Wang, Jinkuan
    [J]. 2011 9TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA 2011), 2011, : 270 - 273
  • [2] Robust Interacting Multiple Model Unscented Particle Filter for Navigation
    Xue, Li
    Han, Yulan
    Na, Chunning
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2020, 2020
  • [3] An interacting multiple model particle filter for manoeuvring target location
    Yang, Ning
    Tian, Weifeng
    Jin, Zhihua
    [J]. MEASUREMENT SCIENCE AND TECHNOLOGY, 2006, 17 (06) : 1307 - 1311
  • [4] Interacting multiple model particle filter optimization resampling algorithm
    [J]. Sun, Tian (suntian@hrbeu.edu.cn), 1600, Beijing University of Aeronautics and Astronautics (BUAA) (43):
  • [5] Interacting Multiple Gaussian Particle Filter
    Qu, Yanwen
    [J]. PROCEEDINGS OF THE 2013 THE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND SOFTWARE ENGINEERING (ICAISE 2013), 2013, 37 : 62 - 66
  • [6] Comparison of Multiple Model Particle Filter and Interacting Multiple Model EKF in Maneuvering Target Tracking
    Yildirim, Berkin
    Demirekler, Mubeccel
    [J]. 2008 IEEE 16TH SIGNAL PROCESSING, COMMUNICATION AND APPLICATIONS CONFERENCE, VOLS 1 AND 2, 2008, : 445 - 448
  • [7] Tracking Algorithm Based on Improved Interacting Multiple Model Particle Filter
    Hailin Feng
    Juanli Guo
    [J]. Journal of Harbin Institute of Technology(New series), 2019, 26 (03) : 43 - 49
  • [8] A New Interacting Multiple Model Algorithm Based on the Unscented Particle Filter
    Deng Xiaolong
    Zhou Pingfang
    [J]. FIFTH INTERNATIONAL CONFERENCE ON INFORMATION ASSURANCE AND SECURITY, VOL 1, PROCEEDINGS, 2009, : 419 - +
  • [9] Adaptive Interacting Multiple Model Unscented Particle Filter Tracking Algorithm
    Liu Hongjiang
    [J]. DIGITAL MANUFACTURING & AUTOMATION III, PTS 1 AND 2, 2012, 190-191 : 906 - 910
  • [10] Interacting Multiple Model-Feedback Particle Filter for Stochastic Hybrid Systems
    Yang, Tao
    Blom, Henk A. P.
    Mehta, Prashant G.
    [J]. 2013 IEEE 52ND ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2013, : 7065 - 7070