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A GNSS/INS Compact Filter and Navigation Method

A filter and tight combination technology, applied in the field of satellite navigation, can solve the problem of low positioning accuracy

Active Publication Date: 2022-08-09
PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to provide a kind of GNSS / INS tightly integrated filter and integrated navigation method, to solve the problem of low positioning accuracy of current GNSS / INS tightly integrated navigation system

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  • A GNSS/INS Compact Filter and Navigation Method
  • A GNSS/INS Compact Filter and Navigation Method
  • A GNSS/INS Compact Filter and Navigation Method

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Embodiment Construction

[0027] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0028] Embodiments of GNSS / INS Compact Combination Filters

[0029]The structure of the traditional GNSS / INS compact filter is as follows figure 1 As shown, the Kalman filter is used, and the Kalman filter is based on the output of the inertial navigation system (INS) (including the INS predicted satellite-to-ground distance and the INS predicted relative velocity between the receiver and the satellite) and the GNSS output (including the GNSS observation pseudorange and multidimensional distance). Puller data) to perform real-time filtering and calculation, output the position, velocity, and attitude error correction values ​​of the INS, and use the correction values ​​to correct the INS predicted position, velocity, and attitude output by the original inertial navigation system.

[0030] The present invention adds a deep neural network model...

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Abstract

The invention relates to a GNSS / INS tight combination filter and a navigation method, belonging to the technical field of satellite navigation. The invention adds a deep neural network model on the basis of the existing GNSS / INS tight combination filter, and uses the deep neural network model to predict the state error of the INS at the current moment according to the pseudorange error of the GNSS and the state error of the INS at the previous moment. , get the state error of the INS at the current moment to correct the Kalman filter, use the INS state error correction value output by the corrected Kalman filter to correct the state quantity of the INS at the current moment, and take the corrected INS state quantity as the current moment. The positioning information of GNSS / INS can be realized in order to realize the compact integrated navigation of GNSS / INS. When the invention uses the deep neural network model to correct the Kalman filter, the error of satellite positioning is fully considered, and the navigation and positioning accuracy of the GNSS / INS tight combination filter is greatly improved.

Description

technical field [0001] The invention relates to a GNSS / INS tight combination filter and a navigation method, and belongs to the technical field of satellite navigation. Background technique [0002] In order to obtain the navigation parameters that the user is interested in, the satellite / inertial integrated navigation system (GNSS / INSIntegrated Navigation System) needs to perform state estimation. The most widely used state estimation method is the Kalman filter algorithm proposed by Kalman RE in 1972. The classical Kalman filter algorithm establishes the state equation based on the dynamic equation of the INS system, and establishes the observation equation based on the GNSS observation model. The accuracy and reliability of its state estimation are determined by the degree of understanding of the INS system dynamics model and the accuracy of the statistical characteristics of the observed noise. If all errors of the INS system and GNSS system can be accurately modeled, ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01C21/16G01C21/20G01S19/47G06N3/04G06N3/08
CPCG01C21/165G01C21/20G01S19/47G06N3/04G06N3/08
Inventor 肖凯孙付平张伦东朱新慧李万里何劢航
Owner PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU