Unlock instant, AI-driven research and patent intelligence for your innovation.

A Relative Integrated Navigation and Positioning Method of Internet of Vehicles Based on Minimum Error Entropy Criterion

A technology that combines navigation and positioning methods, and is used in road network navigators, navigation, surveying and navigation, etc., to achieve the effect of improving accuracy and applicability

Active Publication Date: 2021-09-03
SHANGHAI DIANJI UNIV +1
View PDF1 Cites 0 Cited by
  • 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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A Relative Integrated Navigation and Positioning Method of Internet of Vehicles Based on Minimum Error Entropy Criterion
  • A Relative Integrated Navigation and Positioning Method of Internet of Vehicles Based on Minimum Error Entropy Criterion
  • A Relative Integrated Navigation and Positioning Method of Internet of Vehicles Based on Minimum Error Entropy Criterion

Examples

Experimental program
Comparison scheme
Effect test

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]

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a vehicle network relative combined navigation and positioning method under the minimum error entropy criterion, including a vehicle network system for inter-vehicle communication, and a vehicle positioning device for obtaining navigation observation data; by establishing a target vehicle parameter measurement equation; Under the centralized fusion framework based on the minimum error entropy Kalman filter algorithm, the navigation observation data of the target vehicle is fused to generate the state estimation of the target vehicle; under the distributed fusion framework based on the minimum error entropy Kalman filter algorithm, the target vehicle is fitted And the navigation observation data of adjacent vehicles to generate relative navigation observation data, and generate relative state estimation, so as to fuse the target vehicle state estimation. The invention performs relative combined navigation and positioning on the target vehicle in the non-Gaussian noise state under the minimum error entropy criterion, and improves the accuracy and applicability of the navigation and positioning equipment under the Internet of Vehicles.

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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(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