Improved Neural Network Information Fusion in Integrated Navigation System

In order to overcome the limitation of single sensor in vehicle integrated navigation system, cascade fusion architecture is proposed to enhance the reliability of location information. Our research is focus on the algorithm in decision-making level of the fusion architecture, which is used to fuse the information from Global Positioning System (GPS), Kalman filter and Map Matching (MM) to get the precise location. The proposed algorithm in this paper utilizes Particle Swarm Optimizer (PSO) to substitute the traditional Back-Propagation (BP) algorithm in training parameters of neural net. It has more generalization capability. Besides that, it converges stably and is resistant to local optima compared with traditional BP. Test result shows that the proposed algorithm can improve location accuracy by making full use of all sensors' information, and it is robust and effective.