Distributed Variational Measurement Update for Extended Target Tracking With Random Matrix

The Gamma Gaussian inverse Wishart (GGIW) extended target model leads to an elegant implementation of the Bayesian estimation for the extended target tracking. In this article, we proposed the distributed variational Bayesian measurement update for the GGIW implementation. First, a centralized multi...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on aerospace and electronic systems Vol. 60; no. 4; pp. 3792 - 3806
Main Authors Jiao, Qinqin, Yang, Xiaojun
Format Journal Article
LanguageEnglish
Published IEEE 01.08.2024
Subjects
Bayes methods
Convex functions
Distributed target tracking
Estimation
extended target tracking
Kinematics
Noise measurement
random matrix (RM)
Symmetric matrices
Target tracking
variational Bayes (VB)
Online AccessGet full text

Cover

More Information
Summary:The Gamma Gaussian inverse Wishart (GGIW) extended target model leads to an elegant implementation of the Bayesian estimation for the extended target tracking. In this article, we proposed the distributed variational Bayesian measurement update for the GGIW implementation. First, a centralized multisensor measurement update algorithm for GGIW model was derived using variational Bayesian inference. Then, we proposed the distributed variational measurement update based on the alternating direction method of multipliers (ADMM) algorithm. Furthermore, we introduced a regularization term representing a constraint of the prior information and the optimality of the estimation. We presented a heuristic solver which divided the formulated optimization problem into three subproblems and solved alternately in the framework of ADMM. The simulation results verify the effectiveness and show the comparable performance to that of the centralized algorithm with faster convergence.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2024.3368405