Anti-disturbance fault-tolerant initial aligning method of inertial navigation system

Abstract

The invention relates to an anti-disturbance fault-tolerant initial aligning method of an inertial navigation system. The anti-disturbance fault-tolerant initial aligning method comprises the following steps: building a disturbance observer for estimating and counteracting inertial component drift in a multi-unknown input inertial navigation system or an inertial combination navigation system, designing an adaptive estimator for rebuilding a navigation system, and building a filter with a robust dissipativity performance index and a guaranteed cost performance index, wherein the robust dissipativity performance index and guaranteed cost performance index are used for inhibiting energy bounded disturbance in the system, and optimizing the upper bound of estimation error variance; building an anti-disturbance fault-tolerant filter based on the disturbance observer, the adaptive estimator, the filter with the robust dissipativity performance index and the guaranteed cost performance index; solving a gain array of a robust anti-disturbance fault-tolerant filter based on a convex-optimum method. By adopting the fault-tolerant initial aligning method, the defect of low filtering accuracy in the prior art is overcome. The anti-disturbance fault-tolerant initial aligning method has the advantages of high anti-disturbance performance, high filtering accuracy and high reliability and the like, and can be applied to the inertial navigation system or the inertial combination navigation system and the like.

Description

technical field [0001] The invention relates to an inertial navigation system or an inertial integrated navigation system, in particular to an anti-jamming and fault-tolerant initial alignment method for an inertial navigation system. Background technique [0002] Due to the structural characteristics of the inertial navigation system and the variability of its environment, INS has many unknown inputs such as modeling uncertainty, measurement noise, external environmental interference, sensor error, and system failure. [0003] Before the present invention is made, the traditional method assumes the interference as Gaussian noise, and then adopts the optimal random filtering method. However, when the system contains effects such as non-Gaussian noise, this type of method is very conservative. Changes in system parameters will be caused by INS working environment or temperature changes; aging of integrated circuit systems will cause deviations or failures of inertial devices...

AI Technical Summary

Problems solved by technology

h 2 and H ∞ The filtering method can solve the filtering problem of the system containing Gaussian white noise and model uncertainty at the same time, but the interference suppression is used for the interference with known characteristics, which makes the filtering accuracy not high

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