Adaptive Kalman filtering method of vehicle-mounted microinertia/satellite integrated navigation system

Abstract

The invention discloses an adaptive Kalman filtering method of a vehicle-mounted microinertia/satellite integrated navigation system. By the adaptive Kalman filtering method, when a state estimating error parameter and a measured noise parameter of the integrated navigation system are unknown or time-varying, a measured noise variance matrix and a state noise variance matrix are updated in real time according to measured output and state information while state estimation is performed. An exponential fading memory weighted average method is used for fading influence of the old measured noise and the system noise; moreover, in view of the problem that the noise variance matrix may lose positive definiteness, a sequential filtering method is introduced for the size of each element on a diagonal in the matrix is limited. The adaptive Kalman filtering method is small in calculation amount and high in robustness; and compared with conventional Kalman filtering, the adaptive Kalman filteringmethod has the advantages as follows: by the adaptive Kalman filtering method, the position information, the speed information and the attitude information of a vehicle with higher accuracy can be obtained.

Description

technical field [0001] The invention belongs to the field of intelligent traffic vehicle navigation, and in particular relates to an adaptive Kalman filtering method of a vehicle-mounted micro-inertia / satellite integrated navigation system. Background technique [0002] At present, GPS / INS integrated navigation has been widely used, especially in the military field, but the inertial navigation level inertial sensors that make up the INS (inertial navigation system) are generally relatively expensive, which limits its application in civilians. With the continuous advancement of MEMS (micro-electromechanical systems) technology, low-cost integrated navigation systems have gradually begun to be applied in the civilian field. The Kalman filter, which is widely used in integrated navigation, can only obtain the optimal estimation of the state under the condition that the stochastic dynamic model, structural parameters and noise parameters are known accurately. However, in practi...

AI Technical Summary

Problems solved by technology

This method can not only significantly improve the traditional Kalman filter divergence caused by inaccurate or divergent noise statistical information, but also reduce the calculation amount of traditional adaptive filtering, ensure the positive definiteness of the noise matrix, and effectively improve the accuracy and robustness of integrated navigation.

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