Fault detection of integrated navigation system based on genetic algorithm optimization for deep belief network

Abstract

An accurate fault detection method is critical in preventing the integrity of integrated navigation system from the abnormal measurements which may occur any time. Here, a genetic algorithm optimization for deep belief network fault detection method is proposed, where the system measuring residuals sequence is used as the input, and the output is the system operating state, such as normal or fault types, in pointwise. The proposed technique extracts the features with various scales, which contain both the local and the general information of the signal sequence, for making a comprehensive and precise classification. To show the validity of the proposed method, simulations based on INS/GNSS integrated navigation system are carried out. The simulation results show that the proposed fault detection algorithm and method is superior to the existing algorithms on the faults detection rate and false alarm rate, and thus, system reliability and navigation precision have been greatly improved.