Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2012, Special Issue (2012), Article ID 141727, 20 pages.

Convergence Results for the Gaussian Mixture Implementation of the Extended-Target PHD Filter and Its Extended Kalman Filtering Approximation

Feng Lian, Chongzhao Han, Jing Liu, and Hui Chen

Full-text: Open access

Abstract

The convergence of the Gaussian mixture extended-target probability hypothesis density (GM-EPHD) filter and its extended Kalman (EK) filtering approximation in mildly nonlinear condition, namely, the EK-GM-EPHD filter, is studied here. This paper proves that both the GM-EPHD filter and the EK-GM-EPHD filter converge uniformly to the true EPHD filter. The significance of this paper is in theory to present the convergence results of the GM-EPHD and EK-GM-EPHD filters and the conditions under which the two filters satisfy uniform convergence.

Article information

Source
J. Appl. Math., Volume 2012, Special Issue (2012), Article ID 141727, 20 pages.

Dates
First available in Project Euclid: 3 January 2013

Permanent link to this document
https://projecteuclid.org/euclid.jam/1357178255

Digital Object Identifier
doi:10.1155/2012/141727

Mathematical Reviews number (MathSciNet)
MR2956527

Zentralblatt MATH identifier
1251.93125

Citation

Lian, Feng; Han, Chongzhao; Liu, Jing; Chen, Hui. Convergence Results for the Gaussian Mixture Implementation of the Extended-Target PHD Filter and Its Extended Kalman Filtering Approximation. J. Appl. Math. 2012, Special Issue (2012), Article ID 141727, 20 pages. doi:10.1155/2012/141727. https://projecteuclid.org/euclid.jam/1357178255


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