Combinated navigation method based on robust dissipation filtering

Abstract

The invention discloses a combined navigation method which is based on robust dissipative filtering, and relates to a combined navigation systematic method of strapdown inertia, satellite and astronomy. Firstly, the combined navigation method uses a strapdown inertia navigation system, a GPS receiver and an astronomical navigation system for forming a combined internal navigation system; secondly, according to the dissipative control idea of the modern control theory, the combined navigation method uses a linear matrix inequality (abbreviated as LMI) algorithm for designing a robust dissipative filter so as to implement the fusion of high-precision GPS position and the posture measurement information of speed and the astronomical navigation system; the combined navigation method completely exerts high reliability and usability of traditional navigation means, as well as high precision of satellite and astronomy navigation, and finally achieves the high-reliability and high-precision combined navigation that is based on multiple sensors.

Description

technical field [0001] The invention relates to a combined navigation method, which is a combined navigation method based on robust dissipation filtering, and is applicable to the technical field of aviation navigation, and can be applied to aircraft navigation, and can also be applied to aerospace and navigation navigation positioning. Background technique [0002] The navigation system is to measure and calculate the movement information of the carrier, and provide it to the driver or the autopilot to realize the correct manipulation or control of the carrier. When various navigation systems are used alone, it is difficult to meet the navigation performance requirements. An effective way to improve the overall performance of the navigation system is to use combined navigation technology, that is, use two or more non-similar navigation systems to measure the same navigation information. Measured and resolved to form volume measurements from which the errors of the various n...

AI Technical Summary

Problems solved by technology

Therefore, when the order of the system is very high, the filter will lose its real-time performance, especially when the system has colored noise or modeling errors, it is prone to filter divergence. Although there are improved methods such as EKF and UKF, they are still subject to assumptions Too strong for practical application

H is now based on the Riccati equation ∞ Filtering technology has strong robustness to the uncertainty of system noise, but improper initial parameter setting may still lead to filtering divergence, and parameter selection can only be based on experience without a systematic method, which brings certain difficulties to engineering practice; At the same time, the solution of the Riccati equation is mostly based on the recursive algorithm, which has a large amount of calculation and the convergence may not be guaranteed.

The above methods are filtering methods based on a single performance index, which can only target the variance or H of the system. ∞ Norm optimization, and prone to filter divergence

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