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Combined navigation method of volume Kalman filter and discrete grey model

A Kalman filter and gray model technology, applied in the field of satellite positioning, can solve the problems of increasing system cost and calculation amount, achieve the effect of integrated navigation calculation, improve horizontal positioning accuracy, and suppress errors

Active Publication Date: 2022-05-10
SHANGHAI MARITIME UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, many solutions to occlusion problems in urban environments focus on adding additional equipment, such as the application of SLAM (simultaneous localization and mapping, simultaneous positioning and mapping), visual sensors, etc., or using neural networks to predict occlusion data, which will undoubtedly increase the system’s performance. Cost and Calculations

Method used

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  • Combined navigation method of volume Kalman filter and discrete grey model
  • Combined navigation method of volume Kalman filter and discrete grey model
  • Combined navigation method of volume Kalman filter and discrete grey model

Examples

Experimental program
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Effect test

Embodiment 1

[0051] This embodiment provides a combined navigation method of volumetric Kalman filter and discrete gray model, such as figure 1 and figure 2 shown, including:

[0052]Step S101. Obtain the resolved GNSS data and original IMU data.

[0053] In some embodiments, the resolved GNSS data and original IMU (inertial measurement unit) data are acquired through vector tracking, the GNSS data includes GNSS velocity, position and carrier-to-noise ratio data, and the original IMU data includes three-axis gyroscope and acceleration meter output.

[0054] Step S102. Initialize volume point e, weight w and related parameter matrix according to state dimension n.

[0055] In some cases, the calculation formula of volume point e and weight ω is as follows:

[0056]

[0057]

[0058] Among them, I n×n is an n-dimensional unit matrix, and n is set to 15 in this embodiment.

[0059] The relevant parameter matrix includes the system state vector X k , the prediction covariance P ...

Embodiment 2

[0122] This embodiment provides a combined navigation device of volumetric Kalman filter and discrete gray model, such as Figure 4 shown, including:

[0123] Data acquisition module 201, used to acquire GNSS data and original IMU data that have been resolved;

[0124] The first initialization module 202 is used to initialize volume points, weights and related parameter matrices according to the state dimension;

[0125] The second initialization module 203 is used to obtain the volume point error by initializing the volumetric Kalman filter;

[0126] An update and discrimination module 204, configured to perform time update and CNR discrimination based on the initialized volume point error and related parameter matrix;

[0127] The first calculation module 205 is used to establish a discrete gray model and use the discrete gray model to calculate the height prediction value at time k+1 when the number of satellites whose carrier-to-noise ratio is lower than the preset value...

Embodiment 3

[0131] This embodiment provides a computer-readable storage medium, and a computer program is stored on the computer-readable storage medium. When the computer program is executed by one or more processors, the MEMS-INS-assisted GNSS vector loop tracking method of Embodiment 1 is realized. .

[0132] In this embodiment, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or their combination, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable In addition to programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), Erasable Programmable Read-Only Memory (Erasable Programmable Read-Only Memory, referred to as EPROM), Programmable Read-Only Memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical dis...

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Abstract

The invention provides an integrated navigation method of a volume Kalman filter and a discrete grey model. The integrated navigation method comprises the following steps: acquiring solved GNSS (Global Navigation Satellite System) data and original IMU (Inertial Measurement Unit) data; volume points, weights and related parameter matrixes are initialized according to the state dimensions; a volume point error is obtained through initialization of a volume type Kalman filter; time updating is carried out, and carrier-to-noise ratio discrimination is carried out; under the condition that the number of satellites whose carrier-to-noise ratios are lower than a first preset value is smaller than a second preset value, establishing a discrete gray model and calculating a height prediction value at the k + 1 moment; and performing state updating and volume point updating based on the height predicted value at the k + 1 moment, and calculating a combined navigation result of the global satellite navigation system and the inertial navigation system. According to the method, the error that the precision of the traditional CKF is reduced along with the rising of the system can be reduced, the horizontal positioning precision of the system when GNSS data are missing is improved, the DGM (1, 1) is used for assisting in a vertical channel, the error of a filter in the case of a height positioning result is suppressed, and the method can work better in a shielding environment.

Description

technical field [0001] The invention relates to the technical field of satellite positioning, in particular to a combined navigation method of a volumetric Kalman filter and a discrete gray model. Background technique [0002] GNSS (Global Navigation Satellite System, Global Navigation Satellite System) / INS (Inertial Navigation System, Inertial Navigation System) integrated navigation system alleviates the problem that GNSS is susceptible to occlusion and INS error accumulates over time, using the former's high precision and the latter's The stability has obtained accurate and continuous positioning results, and has been widely used in various fields. At present, many solutions to occlusion problems in urban environments focus on adding additional equipment, such as the application of SLAM (simultaneous localization and mapping, simultaneous positioning and mapping), visual sensors, etc., or using neural networks to predict occlusion data, which will undoubtedly increase the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/16G01C21/20
CPCG01C21/165G01C21/20
Inventor 刘卫史一航胡媛王胜正
Owner SHANGHAI MARITIME UNIVERSITY
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