Application of pseudolinear partitioned filter to passive vehicle tracking

Research output: Contribution to journalArticle

1 Citation (Scopus)

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 - 1984
Externally publishedYes

Fingerprint

Bearings (structural)
Vehicle Tracking
Filter
Sensors
Data compression
Processing
Target tracking
Error analysis
Sensor
Target Tracking
Data Compression
Estimation Error
Parallel Processing
Estimate
Predictors
Partitioning
Monte Carlo Simulation
Fixed point
Interval
Range of data

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Management Science and Operations Research

Cite this

Application of pseudolinear partitioned filter to passive vehicle tracking. / Watanabe, Keigo.

In: International Journal of Systems Science, Vol. 15, No. 9, 1984, p. 959-975.

Research output: Contribution to journalArticle

@article{e6df4df73a534a3397e4a154f2053a25,
title = "Application of pseudolinear partitioned filter to passive vehicle tracking",
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.",
author = "Keigo Watanabe",
year = "1984",
doi = "10.1080/00207728408926615",
language = "English",
volume = "15",
pages = "959--975",
journal = "International Journal of Systems Science",
issn = "0020-7721",
publisher = "Taylor and Francis Ltd.",
number = "9",

}

TY - JOUR

T1 - Application of pseudolinear partitioned filter to passive vehicle tracking

AU - Watanabe, Keigo

PY - 1984

Y1 - 1984

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0021497485&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0021497485&partnerID=8YFLogxK

U2 - 10.1080/00207728408926615

DO - 10.1080/00207728408926615

M3 - Article

AN - SCOPUS:0021497485

VL - 15

SP - 959

EP - 975

JO - International Journal of Systems Science

JF - International Journal of Systems Science

SN - 0020-7721

IS - 9

ER -