Unequal interval federated filter method based on dynamic information distribution

Abstract

The invention discloses an unequal interval federated filter method based on dynamic information distribution, belonging to the technical field of aircraft integrated navigation. The method comprises the following steps: firstly, establishing a fused reset mode federated filter of an aviation vehicle-loaded inertial navigation system and other navigation systems, then constructing an unequal interval federated filter by the combination of the different characteristics of data updating frequency of a practical sensor, and finally, establishing the dynamic information distribution-based unequal interval federated filter based on the number of effective local sub filters, a system covariance matrix, and the information distribution factors of a dynamic observable degree calculating filter. According to the method, under the condition of hardly increasing the extra calculating burden, the sub filters are reset according the number of the effective local sub filters, the system covariance matrix and the information distribution factors of the dynamic observable degree calculating filter, the information of different measuring periods is optimally fused, the information distribution efficiency and information distribution performance of the federated filter calculation method can be improved, and the method has a certain practical value.

Description

technical field [0001] The invention discloses a non-equal interval federation filtering method based on dynamic information allocation, which belongs to the technical field of integrated navigation. Background technique [0002] In the federated filtering structure of the integrated navigation system, the calculation cycles of the local filter and the main filter are generally fixed. However, on the one hand, due to the different data update frequencies of various auxiliary navigation devices, the measurement cycle of each local filter is different, and it may not be possible to provide local estimates to the main filter synchronously, which leads to the problem of unequal intervals of filtering. The general solution is to use the method of extrapolation and interpolation to equalize the non-equally spaced samples before filtering, but this method destroys the originality of the information, and the estimated value of the sample at a certain moment is not equal to the Poss...

AI Technical Summary

Problems solved by technology

However, on the one hand, due to the different data update frequencies of various auxiliary navigation devices, the measurement cycle of each local filter is different, and it may not be possible to provide local estimates to the main filter synchronously, which leads to the problem of unequal intervals in filtering

The general solution is to use the method of extrapolation and interpolation to equalize the non-equally spaced samples before filtering, but this method destroys the originality of the information, and the estimated value of the sample at a certain moment is not equal to the Possible measurement values ​​at any time, thus introducing human error, affecting the filtering accuracy, and increasing the computational burden

[0003] Scholars at home and abroad have proposed improvement measures including proving that its non-equal interval algorithm is equivalent to the centralized Kalman algorithm with a reset structure; the sub-filters sample at the same time and observe the target at the same sampling rate; extrapolation and interpolation There are various methods such as pushing the time of the sub-filter to the fusion time of the main filter, but too many restrictions limit the practical application

After analysis, it is not difficult to find that the existing relevant conclusions still need more in-depth research

Images