A Novel Bayesian-Based Adaptive Algorithm Applied to Unobservable Sensor Measurement Information Loss for Underwater Navigation
Haoqian Huang, Shuang Zhang, Di Wang, Keck Voon Ling, Fan Liu, Xiufeng He
Abstract
In complex underwater environment, it is hard to acquire the accurate position estimation when the sensor measurement information is lost. Under this situation, the measurement loss is unobservable, and meanwhile the statistical information of system noise is unpredictable and time-varying, so existing algorithms have poor performances. To address the problem, the paper proposes a Bayesian-based adaptive Kalman filter with Gaussian-inverse-Wishart mixture distribution (BAKF-GIWM). By using BAKF-GIWM, the measurement loss is judged through the process of sensor measurement, then the system state vector and statistical information of system noise are together determined according to variational Bayesian method. Meanwhile, the estimation results of measurement loss status are regarded as evaluation criteria to improve efficiency of the BAKF-GIWM. The simulations and the real trials results illustrate that the proposed algorithm can efficiently improve the state estimation accuracy in complex underwater environment.