Polar zone centralized filter-based integrated navigation system residual vector fault detection and isolation method

Abstract

The invention provides a polar zone centralized filter-based integrated navigation system residual vector fault detection and isolation method. In view of a polar zone integrated navigation system with a grid inertial navigation system as a basic navigation device, based on a residual X<2> test method, through designing a residual vector, a residual vector X<2> fault detection and isolation methodis designed, fault detection and isolation on a centralized filter are realized, and the reliability of the integrated navigation system based on the centralized filter group for the polar zone is improved. Compared with a federated filter, the centralized filter has better filter precision. The method enables the centralized filter to have the fault detection and isolation capability, and enhancement of the fault-tolerant capability of the integrated navigation system is realized on the premise of not sacrificing the filter estimation accuracy.

Description

technical field [0001] The present invention relates to a method for detecting a fault in a polar region centralized filtering combined navigation system, in particular to a method for detecting and isolating a residual vector fault in a polar region concentrated filtering combined navigation system. Background technique [0002] High-precision and high-reliability navigation technology is one of the important prerequisites for the vehicle to work and sail safely in polar regions, and navigation reliability is a prerequisite for ensuring navigation accuracy. In order to obtain higher reliability of integrated navigation, federated filters capable of fault detection and isolation are often applied to integrated navigation. However, federated filters are not optimal filters. Compared with the federated filter, the centralized Kalman filter with optimal estimation characteristics can provide higher estimation accuracy, but the centralized filter cannot perform fault detection ...

AI Technical Summary

Problems solved by technology

However, federated filters are not optimal filters

Compared with the federated filter, the centralized Kalman filter with optimal estimation characteristics can provide higher estimation accuracy, but the centralized filter cannot perform fault detection and isolation, and the reliability is poor, because the traditional state χ 2 fault detection method or residual χ 2 The fault detection method can detect the fault detection of the integrated navigation measurement information, but it cannot locate and isolate the fault observation information of the centralized filter.

In summary, the existing fault detection and isolation methods cannot isolate the fault observation information filtered in the integrated navigation set with optimal estimation characteristics.

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