Self-adaptive non-trace kalman filtering method of GPS and BDS dual-mode satellite navigation system

Abstract

The invention discloses a self-adaptive non-trace kalman filtering method of a GPS and BDS dual-mode satellite navigation system. The method comprises the steps of 1, performing non-linear system modeling; 2, performing newton motion law scattering, and performing equivalent substitution to meet a model of a non-linear system; and 3, selecting a GNSS working system with relatively optimal horizontal precision by an HDOP self-adaptive optimal selection algorithm to provide GNSS data, substituting the data into the equivalent substitution model of the non-linear system, processing a mean value and non-linear transfer of covariance by a non-trace transformation method, performing Sigma point sampling by a UKF algorithm to solve the uncertainty of noise, estimating and modifying the noise in real time to solve the non-linear characteristic of an integrated navigation system, and deducing a noise statistic estimator to perform real-time estimation and modification on unknown or inaccurate noise statistic based on a maximum likelihood criterion. The method can obviously improve the precision of the GPS and BDS dual-mode satellite navigation system, and has good effects on processing system model non-linearity and noise statistic characteristic uncertainty.

Description

Technical field [0001] The invention relates to a filtering method of a satellite navigation system, in particular to an adaptive unscented Kalman filtering method of a GPS and BDS dual-mode satellite navigation system. Background technique [0002] With the development of society, the increase of vehicles and the increasingly complicated urban traffic conditions, people have put forward higher requirements for the accuracy of positioning and navigation instruments. But so far, all existing systems for positioning and navigation instruments have certain limitations, making these instruments unable to fully meet people's needs. In order to improve urban traffic and meet people's needs, to achieve high-precision, all-weather, and global positioning, a variety of positioning and navigation systems can be combined to maximize strengths and avoid weaknesses to form a multiple organic integrated navigation system, thereby improving the accuracy and reliability of the positioning naviga...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, please refer to the Chinese invention patent application document with the application number 201710724015.6, the publication number 109425876A, and the name "an improved Kalman filter method for improving positioning accuracy". The defect of this technical solution is that for nonlinear problems , the Extended Kalman Filter (EKF) uses Taylor decomposition to linearize the nonlinear model, which is difficult to calculate and has linear errors. Compared with the Unscented Kalman Filter (UKF), the introduction of linear errors reduces the accuracy of the model

Images