Open AccessShape selection partitioning algorithm for Gaussian inverse Wishart probability hypothesis density filter for extended target tracking
Author(s) -
Li Peng,
Ge Hongwei,
Yang Jinlong,
Zhang Huanqing
Publication year - 2016
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2015.0503
Subject(s) - inverse wishart distribution , gaussian , wishart distribution , algorithm , partition (number theory) , filter (signal processing) , selection (genetic algorithm) , inverse , computer science , tracking (education) , probability density function , mathematics , artificial intelligence , pattern recognition (psychology) , computer vision , multivariate statistics , statistics , machine learning , combinatorics , psychology , pedagogy , physics , geometry , quantum mechanics
The Gaussian inverse Wishart probability hypothesis density (GIW‐PHD) filter is a promising approach for tracking an unknown number of extended targets. However, it does not achieve satisfactory performance if targets in different sizes are spatially close and manoeuvring because the partitioning methods are sensitive to manoeuvres. To solve this problem, the authors propose the shape selection partitioning (SSP) measurement partitioning algorithm. The proposed algorithm first calculates potential centres and shapes of targets. It then combines each centre with different shapes to divide measurements into subcells. Accordingly, some candidate partitions can be obtained. Finally, it selects the most likely candidate partition and outputs the corresponding subcells. Simulation results show that the application of SSP to the GIW‐PHD filter can achieve better performance when targets are spatially close and manoeuvring, which leads to a lower optimal subpattern assignment distance and a higher accuracy of the sum of weights.