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An Adaptive δ-GLMB Filter Under Noise Statistics Mismatch

EasyChair Preprint no. 8063

8 pagesDate: May 24, 2022


Aiming at the problem of multi-target tracking under the condition of noises statistics mismatch, an adaptive δ-GLMB filter based on VB approximation is proposed. The Normal-inverse Wishart distribution is used to model the state one-step prediction and prediction error covariance matrix, and the joint distribution of mean and covariance matrix of measurement noise, and the latent variables are described as Gamma distribution. In this paper, the filter density of single target is expressed as the mixture of Normal inverse Wishart inverse Wishart Gamma Gamma (NNIWNIWGG), and its NNIWNIWGG mixture implementation under linear Gaussian condition is given. According to the minimization of Kullback-Leibler divergence, the approximate solution of marginal likelihood function is obtained. Simulation results show that the proposed adaptive δ-GLMB filter has high tracking accuracy in the case of noises statistics mismatch.

Keyphrases: inverse Wishart distribution, multi-target tracking, noise statistics mismatch, Variational Bayesian, δ-GLMB filter

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Feng Lian and Yiru Lian and Liming Hou},
  title = {An Adaptive δ-GLMB Filter Under Noise Statistics Mismatch},
  howpublished = {EasyChair Preprint no. 8063},

  year = {EasyChair, 2022}}
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