Target Tracking Formulation of the SVSF as a Probabilistic Data Association Algorithm

被引：0
|
作者：
Attari, Mina [1 ]
Gadsden, S. Andrew [1 ]
Habibi, Saeid R. [1 ]
机构：
[1] McMaster Univ, Dept Mech Engn, Hamilton, ON L8S 4L7, Canada
来源：
2013 AMERICAN CONTROL CONFERENCE (ACC) | 2013年
关键词：
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：
Target tracking algorithms are important for a number of applications, including: physics, air traffic control, ground vehicle monitoring, and processing medical images. The probabilistic data association algorithm, in conjunction with the Kalman filter (KF), is one of the most popular and well-studied strategies. The relatively new smooth variable structure filter (SVSF) offers a robust and stable estimation strategy under the presence of modeling errors, unlike the KF method. The purpose of this paper is to introduce and formulate the SVSF-PDA, which can be used for target tracking. A simple example is used to compare the estimation results of the popular KF-PDA with the new SVSF-PDA.
引用
收藏
页码：6328 / 6332
页数：5
相关论文
共 50 条
  • [21] Improved Probabilistic Data Association and Its Application for Target Tracking in Clutter
    Ni Longqiang
    Gao Shesheng
    Xue Li
    Ni Longqiang
    2011 INTERNATIONAL CONFERENCE ON ELECTRONICS, COMMUNICATIONS AND CONTROL (ICECC), 2011, : 293 - 296
  • [22] A Hybrid SVSF Algorithm for Automotive Radar Tracking
    Li, Yaowen
    Li, Gang
    Liu, Yu
    Zhang, Xiao-Ping
    He, You
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (09) : 15028 - 15042
  • [23] Target Tracking in Multipath Environments - An Algorithm Inspired by Data Association
    Sathyan, T.
    Humphrey, D.
    Hedley, M.
    FUSION: 2009 12TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION, VOLS 1-4, 2009, : 1650 - 1657
  • [24] Adaptive probabilistic data-association algorithm for tracking in cluttered environment
    Ahmeda, SS
    Harrison, I
    Woolfson, MS
    IEE PROCEEDINGS-RADAR SONAR AND NAVIGATION, 1996, 143 (01) : 17 - 22
  • [25] Multi-Target Tracking on Riemannian Manifolds via Probabilistic Data Association
    Bicanic, Borna
    Markovic, Ivan
    Petrovic, Ivan
    IEEE SIGNAL PROCESSING LETTERS, 2021, 28 : 1555 - 1559
  • [26] Linear multitarget integrated probabilistic data association for multiple detection target tracking
    Huang, Yuan
    Song, Taek Lyul
    Kim, Da Sol
    IET RADAR SONAR AND NAVIGATION, 2018, 12 (09): : 945 - 953
  • [27] Dynamic Joint Probabilistic Data Association Framework for Target Tracking with Ground Robots
    Krishnaswamy, Sriram
    Vitullo, Shane
    Laidler, Will
    Kumar, Mrinal
    2020 AMERICAN CONTROL CONFERENCE (ACC), 2020, : 2076 - 2081
  • [28] Data Association Based Multi-target Tracking Using a Joint Formulation
    Xiang, Jun
    Hou, Jianhua
    Gao, Changxin
    Sang, Nong
    COMPUTER VISION - ACCV 2016, PT IV, 2017, 10114 : 240 - 255
  • [29] Comparison of Nearest Neighbor and Probabilistic Data Association Methods for Non-linear Target Tracking Data Association
    Kenari, Laleh Rabiee
    Arvan, Mohammad Reza
    2014 SECOND RSI/ISM INTERNATIONAL CONFERENCE ON ROBOTICS AND MECHATRONICS (ICROM), 2014, : 47 - 52
  • [30] A multi-space data association algorithm for target tracking systems
    Tian, Hong-wei
    Jing, Zhong-liang
    COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2007, 12 (04) : 608 - 617
12345