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A Carrier Pose Estimation Method Based on Elman Neural Network

A neural network and attitude estimation technology, applied in the field of carrier attitude estimation based on Elman neural network, to achieve the effects of suppressing error accumulation, reducing computational complexity, and enhancing stability

Active Publication Date: 2021-10-22
GUILIN UNIV OF ELECTRONIC TECH
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  • Claims
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Problems solved by technology

[0004] The purpose of the present invention is to provide a carrier attitude estimation method, which aims to solve the gap between the accuracy of the GNSS attitude measurement system and the precision inertial navigation system, and it is not suitable for high-speed dynamic and satellite signal occlusion scenes and inertial navigation system operation. The shortcomings of the two attitude measurement methods need to be given the initial parameters before and need to correct the error in real time during the operation of the system

Method used

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  • A Carrier Pose Estimation Method Based on Elman Neural Network
  • A Carrier Pose Estimation Method Based on Elman Neural Network
  • A Carrier Pose Estimation Method Based on Elman Neural Network

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[0024] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0025] In describing the present invention, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", The orientation or positional relationship indicated by "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than Nothing indicating or implying that a referenced device or element...

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Abstract

The invention discloses an attitude estimation method based on Elman neural network. In order to solve the problem that the carrier phase difference attitude measurement in the attitude measurement system is easily disturbed and the inertial navigation system has error accumulation, the data of the inertial navigation system is used through the Elman neural network. Find the correction values ​​of the three attitude angles of pitch angle, heading angle and roll angle, and use this correction value to correct the attitude angle obtained by carrier phase difference technology, and measure the correction value and the uncorrected attitude The precise attitude of the carrier can be obtained by adding the angles.

Description

technical field [0001] The invention relates to the technical field of posture measurement, in particular to a carrier posture estimation method based on an Elman neural network. Background technique [0002] The attitude measurement of the carrier is one of the important technical applications of GNSS. It can provide the attitude information of the carrier in real time. Using three antennas for GNSS attitude measurement, the heading angle, roll angle and pitch angle of the carrier can be measured. The error generated by the carrier phase difference attitude measurement system will not accumulate over time. However, due to the limitation of the GNSS system, the carrier phase difference attitude measurement system has poor accuracy; the inertial navigation system has strong autonomy and is free from external interference when working. However, the initial parameters need to be given before the operation of the inertial navigation system, and the error needs to be corrected in...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S19/53G01C21/16
CPCG01C21/165G01S19/53
Inventor 王守华黄涛孙希延纪元法江灿辉陈昱均付文涛肖建明
Owner GUILIN UNIV OF ELECTRONIC TECH
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