Multi-objective optimization anti-interference filtering method for SINS/GPS/polarized light combined navigation system

Abstract

The invention relates to a multi-objective optimization anti-interference filtering method for a SINS / GPS / polarized light combined navigation system, and relates to the multi-objective optimization anti-interference filtering method for the SINS / GPS / polarized light combined navigation system of carriers in earth atmosphere, wherein the carriers comprise unmanned plane, ship, vehicles and the like. The method is characterized by: classifying SINS, GPS, polarization sensors, and interferences in the environment, interference modeling for different type interference, designing an interference observer to cancel the interferences that can be modeled, inhibiting Gaussian random variables by H2 optimization technology, and inhibiting norm-bounded unknown variables by H-infinity optimization method. According to the multi-objective optimization anti-interference filtering method, the characteristics of the interferences in the SINS / GPS / polarized light combined navigation system is fully utilized, interference cancellation and interference inhibition are carried out by using the method of designing the interference observer and robust technology, and the method is usable for improving navigation precision of the carriers in the earth atmosphere.

Description

technical field [0001] The invention relates to a multi-target optimization anti-jamming filtering method of a SINS / GPS / polarized light combined navigation system. This method makes full use of the multi-source interference characteristics existing in the SINS / GPS / polarized light integrated navigation system, counteracts the modelable interference by designing an interference estimator, and designs a hybrid robust filter to suppress Gaussian noise and bounded norm Interference can improve the accuracy, reliability and anti-interference ability of the navigation system. Background technique [0002] As the working environment of aircraft in the field of aeronautical engineering becomes increasingly complex, higher and higher requirements are placed on the autonomy, anti-interference and rapid response capabilities of the aircraft, so the filtering accuracy and reliability of the navigation system become more and more important . Limited by hardware technology and cost, it i...

AI Technical Summary

Problems solved by technology

EKF expands the nonlinear model based on Taylor series, and takes the first-order truncation as the approximation of the original state equation and measurement equation. EKF requires the noise to be Gaussian noise and the model is accurate enough. The EKF filtering method has rounding in the process of linearization. Error, so there is a large model error, the system has model uncertainty, and it cannot be well estimated for other disturbances such as non-Gaussian noise, so it has a certain impact on the filtering accuracy

UKF can be directly applied to nonlinear models, but the noise is required to be Gaussian noise, and UKF has a large amount of calculation, which cannot be effectively implemented

The particle filter algorithm is complex, with a large amount of calculation and poor real-time performance, and there are many problems in practical applications

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