Beidou navigation method based on fusion of robust adaptation and extended Kalman filtering

Abstract

The invention relates to a Beidou navigation method based on fusion of robust adaptation and extended Kalman filtering. The Beidou navigation method based on fusion of robust adaptation and extended Kalman filtering includes the steps: a) acquiring positioning data; b) establishing a state observation equation with a rough error; c) establishing state estimation; d) solving an observation value variance matrix; e) acquiring adaptive filtering solution; f) estimating an error variance matrix; g) performing robust adaption filtering. The Beidou navigation method based on fusion of robust adaptation and extended Kalman filtering has the advantages that the Beidou navigation method based on fusion of robust adaptation and extended Kalman filtering is obviously superior to a least square methodand a Kalman filtering method, and has a certain degree of improvement compared with the extended Kalman filtering method, and the error precision is about 5cm, thus satisfying the accuracy requirement for automatic driving of agricultural machinery, being able to be combined with a high-performance controller to effectively control the steering and speed of agricultural machinery, and being ableto achieve the aim of autonomous positioning, automatic track tracking and automatic driving of the agricultural machinery.

Description

technical field [0001] The present invention relates to a Beidou dynamic navigation method, more specifically, to a Beidou navigation method based on robustness adaptive and extended Kalman filter fusion. Background technique [0002] The navigation control technology of agricultural machinery is the basic requirement for the realization of agricultural modernization, and the solution of PVT information (position information, speed information, time information) is the most core part of the Beidou navigation and positioning solution for agricultural machinery. There are certain studies on agricultural machinery navigation and positioning at home and abroad. The University of Illinois in the United States developed a vehicle automatic navigation control system based on RTK-GPS technology; Research, establish the kinematics and dynamics models of agricultural machinery operations, and use Kalman filtering and other algorithms to improve positioning accuracy. At present, the m...

AI Technical Summary

Problems solved by technology

At present, the most basic method of PVT solution is the least squares method, but the results obtained are not accurate enough, have large errors, and are somewhat disordered.

Compared with the least squares method, the accuracy of the Kalman filter method and the extended Kalman filter method is significantly improved, but it cannot meet the high precision requirements of agricultural machinery industries. Aiming at the influence of gross errors, a method that can effectively reduce the influence of gross errors is proposed. Algorithm combining robustness adaptive and extended Kalman filter, focusing on in-depth optimization of Beidou dynamic navigation method to meet the high-precision requirements of agricultural machinery navigation

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