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A Multiple Model Tracking Algorithm Based on an Adaptive Particle Filter

Authors:

Deyong KangAuthors Info & Claims

Asian Journal of Control, Volume 18, Issue 5

Pages 1877 - 1890

https://doi.org/10.1002/asjc.1275

Published: 01 September 2016 Publication History

Abstract

The interacting multiple model based on a particle filter fails to meet the requirements of real-time performance when manoeuvring target tracking by radar due to deficiencies in its high calculation complexity. An improved particle filter based on landscape adaptive particle swarm optimization is proposed. This filter adopts the method of updating inertia weight, using not only local information and global information, but also preventing algorithm trapping in a local optimum, so the filter can find the optimal solution with less iteration. Additionally, an improved tracking model is presented. With the help of systematic resampling, the model can figure out the model index of particles. The experimental results prove that the new tracking algorithm not only improves manoeuvring target tracking accuracy, but also decreases computing complexity.

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Cited By

Mukherjee AChattaraj SChakraborty S(2019)Tuning of evolutionary particle filtering approach for estimation of trajectory deviationAsian Journal of Control10.1002/asjc.201321:4(1566-1575)Online publication date: 17-Jan-2019
https://dl.acm.org/doi/10.1002/asjc.2013
Havangi R(2019)Mobile robot localization based on PSO estimatorAsian Journal of Control10.1002/asjc.200421:4(2167-2178)Online publication date: 10-Feb-2019
https://dl.acm.org/doi/10.1002/asjc.2004

Index Terms

A Multiple Model Tracking Algorithm Based on an Adaptive Particle Filter
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking

Index terms have been assigned to the content through auto-classification.

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cover image Asian Journal of Control

Asian Journal of Control Volume 18, Issue 5

September 2016

366 pages

ISSN:1561-8625

EISSN:1934-6093

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John Wiley & Sons, Inc.

United States

Publication History

Published: 01 September 2016

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Cited By

Mukherjee AChattaraj SChakraborty S(2019)Tuning of evolutionary particle filtering approach for estimation of trajectory deviationAsian Journal of Control10.1002/asjc.201321:4(1566-1575)Online publication date: 17-Jan-2019
https://dl.acm.org/doi/10.1002/asjc.2013
Havangi R(2019)Mobile robot localization based on PSO estimatorAsian Journal of Control10.1002/asjc.200421:4(2167-2178)Online publication date: 10-Feb-2019
https://dl.acm.org/doi/10.1002/asjc.2004

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