In order to improve the accuracy and stability of INS/GNSS integrated navigation system,a method based on improved deep belief network was proposed.Method based on state chi-square test(SCST)was used for real-time detection of integrated navigation systems,and the detection results used as sample data to improve deep belief network(DBN)training.The deep belief network was used to extract deep features and fault classi-fication from the data.Introducing radial basis functions(RBF)as the activation function of the model to im-prove the adaptability of deep belief networks to complex data distributions;using adaptive moment estimation(ADAM)algorithm instead of traditional gradient descent algorithm to improve the accuracy of fault diagnosis.The numerical simulation results showed that the accuracy of the algorithm in fault identification reaches 97%,which could effectively diagnose the fault types of the INS/GNSS integrated navigation system and ensured the smooth operation of the system.
关键词
组合导航系统/故障诊断/深度信念网络/径向基函数/自适应矩估计算法
Key words
integrated navigation system/fault diagnosis/deep belief network/radial basis function/adaptive moment estimation
维普
万方数据