Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Indoor positioning method based on Bayesian iteration improved particle swarm optimization algorithm

A particle swarm optimization and indoor positioning technology, applied in the field of indoor positioning, can solve the problem of increasing positioning errors

Inactive Publication Date: 2021-06-04
HUNAN UNIV
View PDF3 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0011] The purpose of the present invention is to solve the problem that the KNN algorithm in the position estimation stage of the current indoor positioning technology tends to fall into a local optimal solution and increase the positioning error

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
  • Indoor positioning method based on Bayesian iteration improved particle swarm optimization algorithm
  • Indoor positioning method based on Bayesian iteration improved particle swarm optimization algorithm
  • Indoor positioning method based on Bayesian iteration improved particle swarm optimization algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0055] Indoor positioning method based on Bayesian iterative improved particle swarm optimization algorithm, the process includes:

[0056] (1) Acquisition of positioning database and measurement data of unknown nodes collection of:

[0057] Let the unknown node coordinates be , the coordinates of the known experimental point are , collect and save the data of known experimental points for subsequent position estimation calculations, the unit distance between points is set to θ, θ=1m, the total number of experimental points collected in this embodiment =400, the total number of iterations =100 times;

[0058] Collect the actual measured distance data between the unknown node and each experimental point, denoted as ;

[0059] Set the fitness function as the distance between the estimated position of the unknown node and each experimental point and the measured distance The variance of the difference of can be expressed as:

[0060]

[0061] in, is the fitnes...

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 discloses an indoor positioning method based on a Bayesian iteration improved particle swarm optimization algorithm, which is called a BCLPSO algorithm for short, and comprises the following steps of: 1) acquiring a positioning database and acquiring unknown node measurement data di; 2) substituting into a BCLPSO algorithm for calculation, and executing initialization of a particle position vector and a speed vector; 3) calculating a learning probability Pci and acquiring an individual extreme value pbesti,d; 4) calculating a particle posterior probability Pit, and screening an optimal sample exemplart of a current group; 5) updating position vectors and velocity vectors of the particles; and 6) obtaining a convergence condition, judging an iteration process, and obtaining an optimization result. The method is applied to the technical field of indoor positioning, replaces a traditional KNN algorithm to be used for position estimation, solves the problem that the traditional KNN algorithm is prone to falling into a local optimal solution, can inherit and utilize historical information of each particle based on the BCLPSO algorithm, effectively retains diversity of a particle population, and prevents premature convergence caused by neglecting a potential optimal solution, the global optimal positioning point can be better found, and the positioning precision is improved.

Description

technical field [0001] The invention relates to the technical field of indoor positioning, in particular to an indoor positioning method based on a Bayesian iteratively improved particle swarm optimization algorithm. Background technique [0002] Nowadays, due to the development of information technology, artificial intelligence and Internet of Things technology, people's demand for indoor location-based services (ILBS) is increasing day by day. Indoor positioning technology plays a vital role in IoT applications, such as pedestrian navigation, environmental awareness, and smart cities. At present, outdoor location services for civilian use generally use satellite positioning systems, such as GPS positioning systems, which can achieve meter-level positioning accuracy. However, the satellite signal itself has limited penetration ability and is not suitable for indoor positioning. Therefore, in recent years, many researchers have begun to focus on the research of indoor posi...

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 Applications(China)
IPC IPC(8): H04W64/00H04W4/33G06N3/00
CPCG06N3/006H04W64/00H04W4/33
Inventor 孙炜邹群鑫张星罗敏辉
Owner HUNAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com