Fault Diagnosis Algorithm of Integrated Navigation Based on D-S Evidence Theory Fuses Neural Network

Abstract

In the field of fault diagnosis technology of integrated navigation, traditional methods require high precision of system model, and the single neural network detection has the defects of misdiagnosis, missed diagnosis and large error rate. This paper presents a fault diagnosis method based on fusion neural network. The algorithm takes the error data as the input of neural network. Firstly, BP neural network and improved dynamic particle swarm optimization (PSO) BP neural network are used for fault diagnosis, then the detection results of these two neural networks are fused by D-S evidence theory, and finally verified by simulation. The experimental results show that the method can effectively reduce the error rate of fault detection and improve the accuracy and reliability of fault detection.