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An aerospace vehicle in high-speed flight involves distributed multiple fields of view (FoVs) of a star sensor, and the maneuvering of the vehicle requires inertial-aided star pattern matching. Accordingly, an inertial-aided star pattern matching method for distributed multiple FoVs is proposed. First, the observation and fusion principles of distributed multiple FoVs are illustrated. Then, an inertial-aided star pattern matching scheme is designed based on the space-time distribution of the line-of sight (LoS) vector and inertial measurement information. Next, an LoS vector estimation method assisted by inertial information and the dynamic establishment of a distributed local catalog are proposed. Finally, in a simulation, the dynamic establishment of the part catalog improves the efficiency of star pattern matching by reducing the catalog scale, and the usability of inertial-aided star pattern matching is analyzed for different gyro and accelerometer precisions. [ABSTRACT FROM AUTHOR]

An aerospace vehicle in high-speed flight involves distributed multiple fields of view (FoVs) of a star sensor, and the maneuvering of the vehicle requires inertial-aided star pattern matching. Accordingly, an inertial-aided star pattern matching method for distributed multiple FoVs is proposed. First, the observation and fusion principles of distributed multiple FoVs are illustrated. Then, an inertial-aided star pattern matching scheme is designed based on the space-time distribution of the line-of sight (LoS) vector and inertial measurement information. Next, an LoS vector estimation method assisted by inertial information and the dynamic establishment of a distributed local catalog are proposed. Finally, in a simulation, the dynamic establishment of the part catalog improves the efficiency of star pattern matching by reducing the catalog scale, and the usability of inertial-aided star pattern matching is analyzed for different gyro and accelerometer precisions. [ABSTRACT FROM AUTHOR]

The large dynamic and high-speed flight of aerospace vehicle will bring unpredictable conditions to its navigation system, resulting in that its system random noise probability distribution will no longer meet the preconditions of Gaussian distribution preset by the existing filter algorithm, thus reducing the accuracy of the navigation system. So, it is very important to propose an effective method to solve the filter problem of the navigation system in non-Gaussian distribution to improve the accuracy of the navigation system. Therefore, an integrated navigation method of aerospace vehicle based on rank statistics (LRF) has been proposed in this paper. Firstly, based on the flight characteristics of aerospace vehicles, an accurate gravity calculation model has been established to improve the accuracy of system modelling. Then, the state equation and measurement equation of integrated navigation system have been established. In combination with the rank filter algorithm as well as the determined weights, sampling points are calculated and nonlinearly propagated through the transition matrix to achieve an accurate estimation about the predicted values of the state quantities and measurement quantities and the covariance matrix. In turn, it simulates the probability distribution of the system state effectively. Therefore, when the system random noise probability distribution of the aerospace vehicle does not meet the Gaussian distribution due to various interference factors in the actual flight process, the algorithm can simulate the probability distribution of the actual system to the greatest extent, to improve the accuracy of the integrated navigation system and enhance the reliability of the navigation system ultimately. [ABSTRACT FROM AUTHOR]