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Recursive Noise Adaptive Extended Object Tracking by Variational Bayesian Approximation
<span title="">2019</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
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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
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... 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.
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