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Internet of Vehicles relative integrated navigation positioning method under minimum error entropy criterion

A technology that combines navigation and positioning methods, used in road network navigators, navigation, surveying and navigation, etc.

Active Publication Date: 2021-03-19
SHANGHAI DIANJI UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem of combined navigation and positioning under non-Gaussian noise, the present invention provides a vehicle network relative combined navigation and positioning method under the minimum error entropy criterion, through the centralized framework algorithm and distributed The state estimation of the target vehicle is carried out by combining the framework algorithm

Method used

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  • Internet of Vehicles relative integrated navigation positioning method under minimum error entropy criterion
  • Internet of Vehicles relative integrated navigation positioning method under minimum error entropy criterion
  • Internet of Vehicles relative integrated navigation positioning method under minimum error entropy criterion

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Experimental program
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Embodiment 1

[0086] Embodiment 1: said step 1 specifically includes:

[0087] According to the formula (1) and formula (2), the target vehicle parameter measurement equation is established:

[0088] x(k)=F(k,k-1)x(k-1)+w(k-1) (1);

[0089] the y i (k)=H i (k)x(k)+v i (k), i=1, 2, ..., N (2);

[0090] in, Indicates the state vector of the target vehicle at time k, x e (k) represents the eastward position of the target vehicle, x n (k) represents the northward position of the target vehicle, and α(k) represent the speed and azimuth angle of the target vehicle respectively, F(k, k-1) represents the state transition matrix of the system, y i (k)∈R m is the observation information obtained by the target vehicle. h i (k) is the corresponding observation transition matrix, w(k-1) and v i (k) are the amount of process noise and the amount of observation noise, respectively.

[0091] Said step 2 specifically includes: step 2.1: under the condition that the system described in formula...

Embodiment 2

[0152] Embodiment 2: Based on the relative fusion estimation algorithm of the target vehicle state in Embodiment 1, when some adjacent target vehicles cannot be connected due to distance or communication failure, the M number of communication through the Internet of Vehicles at this time becomes M * The relative combined navigation and positioning results of (23) and (24) obtained by adjacent target vehicles are:

[0153]

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Abstract

The invention relates to an Internet of Vehicles relative combination navigation positioning method under a minimum error entropy criterion. The Internet of Vehicles relative combination navigation positioning method involves an Internet of Vehicles system used for inter-vehicle communication and a vehicle-mounted positioning device used for obtaining navigation observation data. The method comprises the following steps of: establishing a target vehicle parameter measurement equation; under a centralized fusion framework based on a minimum error entropy Kalman filtering algorithm, fusing the navigation observation data of the target vehicle so as to generate target vehicle state estimation; and under a distributed fusion framework based on a minimum error entropy Kalman filtering algorithm, fitting the navigation observation data of the target vehicle and adjacent vehicles to generate relative navigation observation data, and generating relative state estimation so as to fuse the stateestimation of the target vehicle. According to the method, relative integrated navigation positioning is performed on the target vehicle in the non-Gaussian noise state under the minimum error entropy criterion so that the accuracy and the applicability of navigation positioning equipment under the Internet of Vehicles can be enhanced.

Description

technical field [0001] The invention relates to the field of intelligent traffic design, in particular to a vehicle network relative combined navigation positioning method under the minimum error entropy criterion. Background technique [0002] Target vehicle navigation and positioning technology is one of the important technical issues in the fields of intelligent transportation and automatic driving. With the development of technologies such as vehicle-to-vehicle communication and communication-based target vehicle control systems, information can be exchanged between target vehicles, which means that the target vehicle integrated navigation and positioning system can obtain more positioning measurements, thereby providing more accurate The state estimation of the target vehicle; [0003] Most of the existing signal processing algorithms are adaptive signal processing methods based on Kalman filtering, which require the target vehicle positioning system to have a Gaussian...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/28G01C21/34
CPCG01C21/28G01C21/3446
Inventor 冯肖亮冯钰新文传博张坤鹏郑玉卿赵广
Owner SHANGHAI DIANJI UNIV