Application of pseudolinear partitioned filter to passive vehicle tracking

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Abstract

The partitioned estimation method is applied to the bearing-only target tracking problem. A pseudolinear partitioned tracking filter is developed initially in the form of recursive processing. It is then shown that such a tracking filter consists mainly of two parts, a ‘ manoeuvre predictor ’, driven by the deterministic own-sensor manoeuvre input, and a ‘ psuedolinear partitioning fixed-point smoother ’, which gives the initial position and speed estimates. Furthermore, by taking into consideration the parallel processing mechanism, a pseudolinear partitioned tracking filter with data compression is proposed to average the bearing data contaminated by the measurement noise. The parametric relationship between r.m.s. estimation error, data compressing (or renovating) interval, measurement noise level, sensor manoeuvre structure and initial range estimate is presented through Monte Carlo simulations.

Original languageEnglish
Pages (from-to)959-975
Number of pages17
JournalInternational Journal of Systems Science
Volume15
Issue number9
DOIs
Publication statusPublished - Sep 1984
Externally publishedYes

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ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications

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