Recursive Noise Adaptive Extended Object Tracking by Variational Bayesian Approximation

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.