Variational Bayesian Algorithms for Maneuvering Target Tracking with Nonlinear Measurements in Sensor Networks

Yumei Hu, Quan Pan, Bao Deng, Zhen Guo, Menghua Li, Lifeng Chen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The variational Bayesian method solves nonlinear estimation problems by iteratively computing the integral of the marginal density. Many researchers have demonstrated the fact its performance depends on the linear approximation in the computation of the variational density in the iteration and the degree of nonlinearity of the underlying scenario. In this paper, two methods for computing the variational density, namely, the natural gradient method and the simultaneous perturbation stochastic method, are used to implement a variational Bayesian Kalman filter for maneuvering target tracking using Doppler measurements. The latter are collected from a set of sensors subject to single-hop network constraints. We propose a distributed fusion variational Bayesian Kalman filter for a networked maneuvering target tracking scenario and both of the evidence lower bound and the posterior Cramér–Rao lower bound of the proposed methods are presented. The simulation results are compared with centralized fusion in terms of posterior Cramér–Rao lower bounds, root-mean-squared errors and the 3 (Formula presented.) bound.

Original languageEnglish
Article number1235
JournalEntropy
Volume25
Issue number8
DOIs
StatePublished - Aug 2023

Keywords

  • distributed fusion
  • Kullback–Leibler divergence
  • natural gradient
  • nonlinear estimation
  • simultaneous perturbation stochastic approximation
  • variational Bayesian optimization

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