A variational Bayesian based robust cubature Kalman filter under dynamic model mismatch and outliers interference

This paper proposes a variational Bayesian-based cubature Kalman filter to solve the state estimation problem of nonlinear discrete-time systems under dynamic model mismatch and outliers interference, which occurs in the maneuvering target tracking system. In the proposed filter, the measurement noise is divided into the normal measurement noise and the outlier noise, and two inverse Wishart distributions with different degree of freedom parameters are used to model the unknown normal measurement noise covariance and the unknown outlier noise covariance, respectively. To overcome outliers interference, the judgment factor which follows the Bernoulli-beta distribution is designed to identify the type of measurement noise. Meanwhile, the input vector which follows the Gaussian distribution is introduced to realize the dynamic model revision. The state, the judgment factor, the normal measurement noise covariance, the outlier noise covariance, and the input vector are jointly estimated under the variational Bayesian framework. The numerical simulation is illustrated to verify the performance of the proposed filter and the corresponding results show that the proposed filter has good state estimation performance and robustness.

Automated summary

This paper proposes a variational Bayesian-based cubature Kalman filter to solve the state estimation problem of nonlinear discrete-time systems under dynamic model mismatch and outliers interference. By dividing the measurement noise into normal measurement noise and outlier noise, and using two inverse Wishart distributions to model the unknown covariance of the normal measurement noise and outlier noise, the proposed filter can effectively handle the effects of dynamic model mismatch and outliers interference.