Multiple Model Ballistic Missile Tracking with State-Dependent Transitions and Gaussian Particle Filtering

Miao Yu, Liyun Gong, Hyondong Oh, Wen Hua Chen, Jonathon Chambers

Research output: Journal article publicationJournal articleAcademic researchpeer-review

40 Citations (Scopus)

Abstract

This paper proposes a new method for tracking the entire trajectory of a ballistic missile from launch to impact on the ground. Multiple state models are used to represent the different ballistic missile dynamics in three flight phases: boost, coast, and re-entry. In particular, the transition probabilities between state models are represented in a state-dependent way by utilizing domain knowledge. Based on this modeling system and radar measurements, a state-dependent interacting multiple model approach based on Gaussian particle filtering is developed to accurately estimate information describing the ballistic missile such as the phase of flight, position, velocity, and relevant missile parameters. Comprehensive numerical simulation studies show that the proposed method outperforms the traditional multiple model approaches for ballistic missile tracking.

Original languageEnglish
Pages (from-to)1066-1081
Number of pages16
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume54
Issue number3
DOIs
Publication statusPublished - Jun 2018

Keywords

  • Ballistic missile (BM) tracking
  • Bayesian inference
  • Gaussian particle filter
  • multiple state models
  • state-dependent transition probabilities

ASJC Scopus subject areas

  • Aerospace Engineering
  • Electrical and Electronic Engineering

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