Variational Bayesian adaptive Kalman filter for asynchronous multirate multi-sensor integrated navigation system

This study considers an asynchronous multirate data integration problem in the linear state space model with unknown and time-varying statistical parameters of the measurement noises. To improve performance of the multirate adaptive Kalman filter algorithm, a multi-sensor adaptive Kalman filtering algorithm based on variational Bayesian approximations has been developed in an asynchronous multirate multi-sensor integrated navigation system. The proposed filtering algorithm estimates measurement noise variances of the sensors adaptively and also it is robust to anomalous measurements of sensors and however, multirate adaptive Kalman filter is required to use an appropriate algorithm for outlier rejection to achieve a reliable and optimal estimation of position, velocity, and orientation. A navigation system composed of a strapdown inertial navigation system along with Doppler velocity log, inclinometer and depth meter with different sampling rates is designed to evaluate performance of multirate error state Kalman filter (MESKF) and multirate adaptive error state Kalman filter (MAESKF) algorithms and the proposed algorithm. Results of two experimental tests show that the average relative root mean square error (RMSE) of the position estimated by the proposed filtering algorithm can be decreased approximately 57% and 36% when compared to that of MESKF and MAESKF algorithms, respectively.