Study On The Aerospace Vehicle Multi-Source Information Fusion Navigation Technology Based On Particle Filter

Aerospace vehicle put forward high requirements for the autonomy, the reliability and the precision of navigation system, due to its special missions and complex flight environment. Multi-source information fusion navigation technology based on INS/GPS/CNS provides a good basis for the realization of aerospace vehicle with high navigation accuracy and high navigation reliability. The core of aerospace vehicle multi-source information fusion navigation technology is information fusion method. Traditional extended Kalman filter algorithm can well realize the fusion of multi-source heterogeneous navigation sensor information, but under the high dynamic environment, its performance will degenerate and even lead to the divergence of Kalman filter due to the linearization technique of first order series expansion of the combination filter model. In addition, under high dynamic environment, the navigation sensor will have some anomalous changes. Taking this into account, it will greatly reduce the performance of multi-source information fusion navigation system, if not do fault detection for the abnormal navigation sensor output.Considering one single navigation system cannot provide high precision navigation information for aerospace vehicle completely and independently, this paper studied the integrated navigation scheme and algorithm of aerospace vehicle multi-source information fusion. In order to satisfy the high precision and high reliability requirements of aerospace vehicle launch phase, the aerospace vehicle integrated navigation scheme and algorithm of multi information fusion based on particle filter is proposed. This paper also establishes a mathematical model of aerospace vehicle multi-source information fusion autonomous navigation system in Launch Inertial Coordinate, which provides the basis for aerospace vehicle multi-source information fusion autonomous navigation system.Traditional extended Kalman filter algorithm has the limit of first order linear approximation and high demand of the noise statistical nature. This paper studied the new nonlinear filtering algorithm based on particle filter, and constructed the model of aerospace vehicle INS/GPS/STAR Sensor multisource information fusion integrated navigation system based on particle filter. It achieved the dynamic estimation of navigation system's parameters in Launch Inertial Coordinate. The simulation results show that the particle filter algorithm has higher navigation accuracy compared with the extended Kalman filter algorithm, which provide the theoretical support for the realization of aerospace vehicle nonlinear filtering navigation algorithm.Aerospace vehicle navigation system will have unpredictable sensor fault for long-running. This paper analyzes the common fault detection algorithm, and focuses on the likelihood function fault detection algorithm and state estimation residual fault detection algorithm based on particle filter. The results show that the two fault detection algorithms can detect the navigation sensor fault sensitively, and solve the sensor access problems of multi-source information fusion in navigation system effectively. This lays a theoretical basis for the aerospace vehicle high reliability navigation requirements of long-running.In the study of aerospace vehicle multi-source information fusion filtering algorithm and fault detection algorithm based on the particle filter, this paper designs semi physical simulation environment of aerospace vehicle INS/GPS/CNS multi-source information fusion navigation system based on PC104 Embedded Computer. And the corresponding algorithm verification software and hardware platform is constructed. The comprehensive analysis and validation of the performance of the designed particle filter algorithm and fault detection algorithm is carried out on the platform, which provides a useful reference for the engineering application of the multi-source information fusion integrated navigation algorithm.