State Estimation of Infrared Target using Particle Filter

被引：0
|
作者：
Chen, Zidu [1 ]
Liu, Xiaoming [1 ]
Chen, Wanchun [1 ]
Zhou, Tao [1 ]
机构：
[1] Beihang Univ, Sch Astronaut, Beijing 100191, Peoples R China
来源：
2016 IEEE CHINESE GUIDANCE, NAVIGATION AND CONTROL CONFERENCE (CGNCC) | 2016年
基金：
中国国家自然科学基金;
关键词：
TRACKING;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A filtering algorithm for estimating the position of a moving target using information collected by multiple imaging sensors is presented. Based on camera model and theory of particle filter, state transition model and observation model are set up. Weights threshold is proposed to solve the real-time tracking problem in occlusion issue. The experimental positioning system has been set up to test the algorithm, and the results show that the algorithm is effective in tracking moving target. The estimation error in position of the proposed algorithm is within 0.0082m in tracking stationary target and 0.01m for moving target. State estimation based on PF keeps stable in occlusion condition and is much better than Kalman filter (KF) and extended Kalman filter (EKF) which have large volatility. The experiments results show that the algorithm has good precision and anti-interference performance.
引用
收藏
页码：2399 / 2405
页数：7
相关论文
共 50 条
  • [31] Beamforming Using Conventional and Particle Filter Based Algorithms for Radar Target DOA Estimation
    Nagy, Lajos
    2015 9th European Conference on Antennas and Propagation (EuCAP), 2015,
  • [32] On the choice of importance distributions for unconstrained and constrained state estimation using particle filter
    Prakash, J.
    Patwardhan, Sachin C.
    Shah, Sirish L.
    JOURNAL OF PROCESS CONTROL, 2011, 21 (01) : 3 - 16
  • [33] Radar Target Detect using Particle Filter
    Wu, Zhaoping
    Su, Tao
    2010 IEEE RADAR CONFERENCE, 2010, : 955 - 958
  • [34] Maneuvering target tracking by using particle filter
    Ikoma, N
    Ichimura, N
    Higuchi, T
    Maeda, H
    JOINT 9TH IFSA WORLD CONGRESS AND 20TH NAFIPS INTERNATIONAL CONFERENCE, PROCEEDINGS, VOLS. 1-5, 2001, : 2223 - 2228
  • [35] Detection and tracking of a moving point target in infrared image sequences using auxiliary particle filter
    Liu, Zhijun
    Xie, Shengli
    Ren, Xianyi
    PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2008, : 2530 - +
  • [36] On the state estimation of chaotic systems by a particle filter and an extended Kalman filter
    Mejri, Sameh
    Tlili, Ali Sghaier
    Braiek, Naceur Benhadj
    2014 11TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS & DEVICES (SSD), 2014,
  • [37] State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter
    Zhang, Hongjuan
    Franssen, Harrie-Jan Hendricks
    Han, Xujun
    Vrugt, Jasper A.
    Vereecken, Harry
    HYDROLOGY AND EARTH SYSTEM SCIENCES, 2017, 21 (09) : 4927 - 4958
  • [38] A particle swarm optimized particle filter for nonlinear system state estimation
    Tong, Guofeng
    Fang, Zheng
    Xu, Xinhe
    2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6, 2006, : 438 - +
  • [39] A Particle Filter Based Algorithm for State estimation of Dim Moving Point Target in IR Image Sequence
    Tursun, Dilmurat
    Xiang Guiying
    Hamdulla, Askar
    2008 INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY APPLICATION, VOL II, PROCEEDINGS, 2008, : 127 - 131
  • [40] Particle Filter Based State Estimation of Power System
    Doshi, Dipesh
    Chankaya, Mukul
    2017 INTERNATIONAL CONFERENCE ON RECENT TRENDS IN ELECTRICAL, ELECTRONICS AND COMPUTING TECHNOLOGIES (ICRTEECT), 2017, : 77 - 82
12345