An Adaptive Kalman Filter Method Based on Vector Tracking under Ionospheric Scintillation

Abstract

The invention discloses an ionospheric scintillation vector tracking-based self-adapting Kalman filtering method. The ionospheric scintillation vector tracking-based self-adapting Kalman filtering method comprises performing model estimation on the state equation and the measurement equation of a navigation system and performing joint correlation treatment on the tracking results and the navigation calculation results of all channels through self-adapting Kalman filtering, and during the processes above, performing optimal filtering estimation on system noise covariance and measured noise covariance and recurring and updating an error covariance matrix. By acquiring PVT (position, velocity and time) estimation of a next step on the basis of a current state estimation computation time updated equation, the ionospheric scintillation vector tracking-based self-adapting Kalman filtering method reduces system model errors caused by ionospheric scintillation, inhibits filtering divergence, improves filtering precision, enhances system anti-interference capacity and dynamic properties and reduces signal power required for capturing.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to an adaptive Kalman filtering method based on vector tracking in the case of ionospheric scintillation. Background technique [0002] When the GPS signal propagates from the satellite to the earth, it will be interfered by the scintillation of the ionosphere, and the amplitude and phase of the signal will be strongly jittered, which will cause the carrier phase-locked loop of the GPS receiver to lose the lock on the signal, and the phase-locked loop slip will cause the signal to lose its lock. The navigation positioning error increases or even the navigation is interrupted. The traditional STL (scalar tracking loop) processes the signals of the respective satellites alone, and finally solves the navigation results by combining the tracking results of all channels. The performance of scalar tracking loop is affected by loop parameters such as order and n...

AI Technical Summary

Problems solved by technology

However, since all channel signals are correlated, the loss of a single channel will affect the output of the entire system

And this method requires that the dynamic system parameters and measurement models are known, but these variables are difficult to obtain accurately in the case of ionospheric scintillation

Images