Missile Interception Guidance With Parameter Uncertainties Using Desensitized Extended Kalman Filter

Jingsong Yang*, Wei Hu, Tianhao Liu, Lingguo Cui, Jia Liang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

The missile interception problem is considered in this article. As in practical applications, the real states are not available due to the existence of measurement noises and model parameter uncertainties, desensitized extended Kalman filter (DEKF) is applied to generate reliable state estimations. Compared to standard extend Kalman filter (EKF), this approach calculates state estimations by optimizing a cost function with one additional term, which reflects the state estimate error sensitivities. Such a design makes the filter less sensitive to model parameter uncertainties and can be considered as a generalization of the standard EKF. Simulation studies are conducted to evaluate the performance of DEKF when applying to integrated missile-target interception model.

Original languageEnglish
Title of host publicationICSAI 2022 - 8th International Conference on Systems and Informatics
EditorsShaowen Yao, Zhenli He, Zheng Xiao, Wanqing Tu, Kenli Li, Lipo Wang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665474481
DOIs
Publication statusPublished - 2022
Event8th International Conference on Systems and Informatics, ICSAI 2022 - Kunming, China
Duration: 10 Dec 202212 Dec 2022

Publication series

NameICSAI 2022 - 8th International Conference on Systems and Informatics

Conference

Conference8th International Conference on Systems and Informatics, ICSAI 2022
Country/TerritoryChina
CityKunming
Period10/12/2212/12/22

Keywords

  • Desensitized extended Kalman filter
  • Estimation sensitivity
  • Missile-target interception
  • State estimation
  • Uncertain parameter

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