Open AccessCardinalised probability hypothesis density tracking algorithm for extended objects with glint noise
Author(s) -
Li Cuiyun,
Wang Rong,
Hu Yuhen,
Wang Jinbin
Publication year - 2016
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2016.0004
Subject(s) - noise (video) , covariance , algorithm , tracking (education) , bayesian probability , probability distribution , computer science , probability density function , posterior probability , noise measurement , artificial intelligence , mathematics , pattern recognition (psychology) , statistics , noise reduction , image (mathematics) , psychology , pedagogy
The authors present a novel cardinalised probability hypothesis density (CPHD) algorithm under the assumption of a glint measurement noise model with unknown inverse covariance. Noise parameters are assumed to have a Gamma prior distribution so that the predicted and updated PHDs can have mixture of Gaussians representations. A variational Bayesian expectation maximisation procedure is applied to iteratively estimate parameters of the mixture distributions through random hypersurface model CPHD prediction and update steps. Simulation results show that the proposed algorithm can adaptively track extended objects with unknown object number and glint measurement noise, while achieving higher precision compared against the traditional approach.