Vehicle gnss/ins integrated navigation method based on discrete gray neural network model

A technology of gray neural network and integrated navigation, which is applied in the field of vehicle GNSS/INS integrated navigation based on discrete gray neural network model, can solve the problems of reduced navigation accuracy and achieve the effects of small amount of calculation, simple implementation, and few training samples

Active Publication Date: 2021-09-28
SOUTHEAST UNIV
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Problems solved by technology

[0007] Purpose of the invention: the present invention proposes a vehicle GNSS / INS integrated navigation method based on a discrete gray neural network model for the problem of reduced navigation accuracy when the GNSS signal fails

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  • Vehicle gnss/ins integrated navigation method based on discrete gray neural network model
  • Vehicle gnss/ins integrated navigation method based on discrete gray neural network model
  • Vehicle gnss/ins integrated navigation method based on discrete gray neural network model

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Embodiment Construction

[0076] The technical solution of the present invention will be further introduced below in combination with specific implementation methods and accompanying drawings.

[0077] This specific embodiment discloses a vehicle GNSS / INS integrated navigation method based on a discrete gray neural network model, comprising the following steps:

[0078] S1: According to the angular increment and specific force output by the micro-inertial device, use the inertial navigation value update algorithm to solve the attitude, speed and position of the vehicle;

[0079] S2: Establish a discrete gray scale prediction model based on DGM(1,1);

[0080] S3: Improved multi-layer neural network MLP;

[0081] S4: Design a hybrid intelligent prediction algorithm DGM-MLP based on a discrete gray-scale neural network. When the GNSS signal is valid, use DGM-MLP to train the GNSS position; when the GNSS signal is invalid, use DGM-MLP to train the GNSS position. Prediction, obtain pseudo GNSS position in...

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Abstract

The invention discloses a vehicle GNSS / INS integrated navigation method based on a discrete gray neural network model. Attitude, velocity and position; S2: Establish a discrete grayscale prediction model based on DGM(1,1); S3: Improve multi-layer neural network MLP; S4: Design a hybrid intelligent prediction algorithm DGM‑MLP based on discrete grayscale neural network; S5: Using the inertial navigation error equation as the state equation, the difference between the position calculated by INS and the position of GNSS is the observation or the difference between the position calculated by INS and the position of pseudo-GNSS is the observation, and the Kalman filter KF is used for integrated navigation The system performs state estimation; S6: The position, velocity, and attitude errors estimated by the Kalman filter KF are used to output and correct the inertial navigation solution results, and the gyro and meter addition errors are used to feedback and correct the inertial navigation. The invention can effectively solve the problem of reduced navigation accuracy when the GNSS signal fails.

Description

technical field [0001] The invention relates to a vehicle integrated navigation technology, in particular to a vehicle GNSS / INS integrated navigation method based on a discrete gray neural network model. Background technique [0002] The MEMS-INS / GNSS integrated navigation system has been more and more widely used due to its advantages of low cost and miniaturization, but the GNSS signal is easily blocked by tall buildings, bridges or tunnel lights and becomes invalid. When the GNSS signal fails, the integrated navigation system is in a pure inertial navigation state. Due to the low accuracy of MEMS-INS, the solution results will decrease rapidly or even diverge. In order to improve the navigation accuracy when the GNSS signal fails and ensure the reliability of the system, the main methods are as follows: [0003] 1. Add additional sensors, such as machine vision and Doppler speedometer. This method can effectively improve navigation accuracy, but it will lead to increased...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01C21/16G01S19/48
CPCG01C21/165G01S19/48
Inventor 王立辉张月新乔楠石佳晨
Owner SOUTHEAST UNIV
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