Adaptive cubature Kalman filter based on variational Bayesian inference under measurement uncertainty

A novel variational Bayesian inference based on adaptive cubature Kalman filter(VBACKF)algorithm is proposed for the problem of state estimation in a target tracking system with time-varying measurement noise and random measurement losses. Firstly, the Inverse-Wishart(IW) distribution is chosen to model the covariance matrix of time-varying measurement noise in the cubature Kalman filter framework. Secondly, the Bernoulli random variable is introduced as the judgement factor of the measurement losses, and the Beta distribution is selected as the conjugate prior distribution of measurement loss probability to ensure that the posterior distribution and prior distribution have the same function form. Finally, the joint posterior probability density function of the estimated variables is approximately decoupled by the variational Bayesian inference, and the fixed-point iteration approach is used to update the estimated variables. The simulation results show that the proposed VBACKF algorithm considers the comprehensive effects of system nonlinearity, time-varying measurement noise and unknown measurement loss probability, moreover, effectively improves the accuracy of target state estimation in complex scene.