Vehicle GNSS/INS integrated navigation method based on discrete gray neural network model

Abstract

The invention discloses a vehicle GNSS / INS integrated navigation method based on a discrete gray neural network model. The method comprises the following steps: S1: solving the attitude, speed and position of a vehicle by using an inertial navigation value update algorithm according to the angular increment and specific force output by a micro inertial device; S2: establishing a discrete gray prediction model based on DGM (1,1); S3: improving a multi-layer neural network MLP; S4: designing a discrete gray neural network-based hybrid intelligent prediction algorithm DGM-MLP; S5: estimating thestate of a combined navigation system by using an inertial navigation error equation as the state equation, the difference between the position calculated by an INS and the position of a GNSS as an observed quantity or the difference between the position calculated by the INS solution and a pseudo position of the GNSS, and a Kalman filter KF; S6: performing output correction on the inertial navigation calculation result based on the position, speed and attitude errors estimated by the Kalman filter KF, and performing feedback correction on the inertial navigation based on a gyro and a meter error. The method can effectively solve the problem that the navigation precision is reduced when the GNSS signal loses efficacy.

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...

AI Technical Summary

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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