Feature aided monte carlo probabilistic data association filter for ballistic missile tracking

Onur Ozdemir, Ruixin Niu, Pramod K. Varshney, Andrew L. Drozd, Richard Loe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The problem of ballistic missile tracking in the presence of clutter is investigated. Probabilistic data association filter (PDAF) is utilized as the basic filtering algorithm. We propose to use sequential Monte Carlo methods, i.e., particle filters, aided with amplitude information (AI) in order to improve the tracking performance of a single target in clutter when severe nonlinearities exist in the system. We call this approach "Monte Carlo probabilistic data association filter with amplitude information (MCPDAF-AI)". Furthermore, we formulate a realistic problem in the sense that we use simulated radar cross section (RCS) data for a missile warhead and a cylinder chaff using Lucernhammer,1 a state of the art electromagnetic signature prediction software, to model target and clutter amplitude returns as additional amplitude features which help to improve data association and tracking performance. A performance comparison is carried out between the extended Kalman filter (EKF) and the particle filter under various scenarios using single and multiple sensors. The results show that, when only one sensor is used, the MCPDAF performs significantly better than the EKF in terms of tracking accuracy under severe nonlinear conditions for ballistic missile tracking applications. However, when the number of sensors is increased, even under severe nonlinear conditions, the EKF performs as well as the MCPDAF.

Original languageEnglish (US)
Title of host publicationMultisensor, Multisource Information Fusion
Subtitle of host publicationArchitectures, Algorithms, and Applications 2011
DOIs
StatePublished - Jul 13 2011
EventMultisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2011 - Orlando, FL, United States
Duration: Apr 27 2011Apr 28 2011

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8064
ISSN (Print)0277-786X

Other

OtherMultisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2011
CountryUnited States
CityOrlando, FL
Period4/27/114/28/11

Keywords

  • Data association
  • Markov chain Monte Carlo
  • Target tracking

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Ozdemir, O., Niu, R., Varshney, P. K., Drozd, A. L., & Loe, R. (2011). Feature aided monte carlo probabilistic data association filter for ballistic missile tracking. In Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2011 [806406] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8064). https://doi.org/10.1117/12.886278