A New Navigation Data Fusion Method Based on Entropy Coefficient Algorithm for Underwater Vehicles

This paper is focused on the problem that the increasing noise of state equations and observation equations lead to the divergence of Kalman filtering in the dynamic positioning navigation. A new method is put forward to calculate navigation fused weight. The new method is based on information entropy and adopts Gaussian mixture model as the framework. First, the main structure of underwater integrated navigation system and related state and observation equations of every sub-filters are provided. Then we studied the calculation method of the information entropy of each sub-filter, and the concept of entropy product is defined to calculate the weight of each component in the Gaussian mixture model. Finally, the computational process of the Gaussian mixture model filtering algorithm for entropy weighted underwater navigation information fusion is summarized. The simulation experiments show that the precision of the new method is much higher and the inhibiting ability against filtering divergence caused by noise is stronger than weighted Kalman filtering algorithm.