A novel algorithm for tracking targets using manoeuvre models

We present a novel algorithm for robust tracking of targets in challenging scenarios by modelling their high level behaviour. The particle-filter [6] based algorithm combines the benefits of variable-rate filtering [5] with a variable- dimension, manoeuvre-based representation of the target trajecto...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications S. 141 - 148
Hauptverfasser: Gilholm, K, Everett, N.O
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Stevenage IET 2008
Schlagworte:
Filtering methods in signal processing
Markov processes
Monte Carlo methods
Signal processing theory
reversible-jump Markov Chain Monte Carlo
resample-move fashion
Monte Carlo methods
manoeuvre models
variable-rate filtering
target trajectory
target tracking
robust target tracking
Markov processes
particle filtering (numerical methods)
particle-filter based algorithm
ISBN:0863419100, 9780863419102
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:We present a novel algorithm for robust tracking of targets in challenging scenarios by modelling their high level behaviour. The particle-filter [6] based algorithm combines the benefits of variable-rate filtering [5] with a variable- dimension, manoeuvre-based representation of the target trajectory. Variable-rate filtering allows the algorithm to model manoeuvre changes at arbitrary (non-measurement) times, while manoeuvre-based modelling adds to track robustness by providing a parsimonious representation of the target trajectory. The efficiency of the particle filter has been greatly enhanced using reversible-jump Markov Chain Monte Carlo [7] moves that are applied in a Resample-Move fashion [4]. We demonstrate the performance of the algorithm on some example representative scenarios.
ISBN:0863419100
9780863419102
DOI:10.1049/ic:20080066