Asynchronous data fusion for AUV navigation via heuristic fuzzy filtering techniques

This paper presents a heuristic fuzzy position estimation technique for autonomous underwater vehicle navigation. The heuristic estimator performs asynchronous data fusion of all sensor measurements based on their relative confidence levels, and then nonlinearly combines the fused information with the INS estimates via fuzzy filtering techniques. In this paper, the basis and implementation of the estimator will be described, and navigation results will be presented based on the heuristic estimator. In addition, performance comparison based on the heuristic estimator and those based on extended Kalman filters will be reported in our companion paper, and the results are expected to provide insights into the pros and cons of individual methods in terms of computational cost, steady-state and convergence characteristics for bias estimation.