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Recursive Noise Adaptive Extended Object Tracking by Variational Bayesian Approximation
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DOI：10.1109/ACCESS.2019.2947766.png)
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Abstract
En 中文
This paper proposes an adaptive extended object tracking algorithm with unknown time-varying 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 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.
Keywords:
Covariance matrices
Noise measurement
Kinematics
Bayes methods
Object tracking
Adaptation models
Density measurement
Extended object tracking
random matrix
variational Bayes
inverse Wishart distribution
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