Weighted Maximum Correntropy Criterion-Based Interacting Multiple-Model Filter for Maneuvering Target Tracking

Liangliang Huai, Bo Li, Peng Yun, Chao Song, Jiayuan Wang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

During the process of maneuvering target tracking, the measurement may be disturbed by outliers, which leads to a decrease in the state estimation performance of the classic interacting multiple-model (IMM) filter. To solve this problem, a weighted maximum correntropy criterion (WMCC)-based IMM filter is proposed. In the proposed filter, the fusion state is used as the input of each sub-model to reduce the computational complexity of state interaction and the WMCC is adopted to derive the sub-model state update and state fusion to improve the state estimation performance under outlier interference. Through principal analysis, the superiority of the proposed filter over the classic IMM filter in fusion strategy is revealed. The specific form of the proposed filter in radar maneuvering target tracking is provided. Two experimental cases of maneuvering target tracking are tested to illustrate the effectiveness of the proposed filter.

Original languageEnglish
Article number4513
JournalRemote Sensing
Volume15
Issue number18
DOIs
StatePublished - Sep 2023

Keywords

  • interactive multiple model
  • maneuvering target tracking
  • outlier interference
  • weighted maximum correntropy criterion

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