An Adaptive Variational Bayesian Algorithm for Measurement Loss for Underwater Navigation

The marine environment is changeable and complex, and it is difficult but indispensable to study the complex and time-varying environment. The measurement loss has an effect on obtaining the high accuracy navigation information. This paper proposes an adaptive variational Bayesian filter (AVBF) algorithm which takes advantages of the variational Bayesian approach and Kalman filter to deal with the problems of the measurement loss. The proposed AVBF is proved in theory and verified by simulation experiments. Owing to the characteristics of the variational Bayesian approach, the higher precise state information can be acquired by the AVBF compared with the traditional Kalman filter.