Abstract
This paper proposes a robust Poisson multi-Bernoulli mixture (PMBM) filter with inaccurate process and measurement noise covariances. A derivation of the robust PMBM filter is provided for jointly estimating the kinematic state, the predicted state covariance, and the measurement noise covariance. By modeling the augmented state as a Gaussian inverse Wishart inverse Wishart (GIWIW) distribution, a computationally feasible implementation of the robust PMBM (GIWIW-PMBM) filter is given for linear Gaussian systems. To guarantee the conjugacy of the GIWIW distribution, the variational Bayesian (VB) approach is employed to approximate the posterior density. Finally, simulation results show that the GIWIW-PMBM filter has the best overall performance compared to existing state-of-the-art filters regarding computational cost and filtering performance.
Highlights
Many algorithms have been exploited to solve multitarget tracking (MTT) [1]–[3] problems, which are extremely important and complicated, in a variety of fields
The main contributions of this paper are as follows: (a) A novel, robust MTT method based on the Poisson multi-Bernoulli mixture (PMBM) conjugate prior is proposed under the circumstance of inaccurate process and measurement noise covariances
The robust PMBM filter recursions consist of the prediction and update steps, which are given in Proposition 1 and Proposition 2, respectively
Summary
Many algorithms have been exploited to solve multitarget tracking (MTT) [1]–[3] problems, which are extremely important and complicated, in a variety of fields. The inverse gamma distribution has been chosen for MTT applications that only consider inaccurate measurement noise covariance, such as the robust PHD [25], [26], CBMeMBer [27] and LMB [28], [29] filters. To the best of our knowledge, this is the first work to solve the problem of inaccurate process and measurement noise covariances in the PMBM filter. (a) A novel, robust MTT method based on the PMBM conjugate prior is proposed under the circumstance of inaccurate process and measurement noise covariances.
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