A novel algorithm for tracking a maneuvering target in clutter

An algorithm for tracking a maneuvering target under measurement origin uncertainties is derived based on the approximation and propagation of the target state posterior distribution by combining Bayesian decision theory and suitable hypothesis merging procedures. Simulation results show that the ma...

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Bibliographic Details
Published in:Digital signal processing 2022-06, Vol.126, p.103481, Article 103481
Main Authors: Lucena de Souza, Marcelo, Gaspar Guimarães, Alberto, Leite Pinto, Ernesto
Format: Article
Language:English
Subjects:
Bayesian methods
Clutter
Data association
IMMPDAF
Maneuvering target tracking
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Summary:An algorithm for tracking a maneuvering target under measurement origin uncertainties is derived based on the approximation and propagation of the target state posterior distribution by combining Bayesian decision theory and suitable hypothesis merging procedures. Simulation results show that the main feature of this algorithm is its ability to significantly reduce the estimation error during non-maneuvering periods, making it quite suitable for tracking low maneuvering aircrafts. At the same time, the proposal is able to keep very low levels of track loss rate even for scenarios with high false alarm probability and trajectories with high degree of maneuverability. This proposal presents overall superiority when compared to the Interacting Multiple-Model with Probabilistic Data Filtering (IMMPDAF) solution, with an affordable increase in computational cost.
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2022.103481