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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.

Robust Poisson Multi-Bernoulli Mixture Filter With Inaccurate Process and Measurement Noise Covariances