Positioning Parameter Determination Based On Statistical Regression Applied To Autonomous Underwater Vehicle

The underwater environment is complex and changeable, and it is hard but irreplaceable to research the time-varying noises that have a significant influence on navigation information determination with higher accuracy. To solve the problems of the inaccurate noise information, this paper proposes a novel statistical regression adaptive Kalman filtering (SRAKF) algorithm that makes better use of the merits of the expectation maximization and unscented transformation. The SRAKF is verified from theoretical perspectives, and meanwhile, the stability and accuracy of the algorithm are evaluated by real lake trials. Relying on the properties of the statistical linear regression and the positioning parameter estimation of latent variables, higher precise positioning parameters can be acquired by the SRAKF, even for the measurement noise values with great variation. Hence, the performance of SRAKF is more useful in underwater positioning applications than other traditional algorithms due to its stronger robustness and higher accuracy.