Recursive Noise Adaptive Extended Object Tracking by Variational Bayesian Approximation

Zhifei Li, Jianyun Zhang, Jiegui Wang, Qingsong Zhou
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
This paper proposes an adaptive extended object tracking algorithm with unknown timevarying sensor error covariance in linear state-space models. The proposed algorithm employs Inverse Wishart distribution to describe the full covariance. To produce an analytical solution, the measurement likelihood function introduces a latent variable to obtain an augmented form. Then, the latent variable is involved into the estimated list of quantities. To hold a recursive estimation framework, the proposed
more &raquo; ... algorithm selects variational Bayesian (VB) inference to approximate the joint posterior distribution of estimated quantities. The VB inference minimizes Kullback-Leibler divergence between the true and approximate posterior density to obtain a convergent solution. Simulation experiments with unknown covariance demonstrate the effectiveness of the proposed algorithm. INDEX TERMS Extended object tracking, random matrix, variational Bayes, inverse Wishart distribution.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2947766">doi:10.1109/access.2019.2947766</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/obrzgqk3mnaqbim6sz4kuefjzq">fatcat:obrzgqk3mnaqbim6sz4kuefjzq</a> </span>
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