Fingerprint map matching method based on Euclidean distances

A technology of Euclidean distance and matching method, which is applied in the field of Euclidean distance fingerprint matching, can solve the problems of the influence of positioning results and low positioning accuracy

Inactive Publication Date: 2014-02-19
HARBIN INST OF TECH
View PDF2 Cites 38 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention is to solve the problem that the positioning accuracy of the traditional WKNN algorithm is low, and the value of k will have a great influence on the positioning result, and provides a fingerprint map matching method based on Euclidean distance

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
  • Fingerprint map matching method based on Euclidean distances
  • Fingerprint map matching method based on Euclidean distances
  • Fingerprint map matching method based on Euclidean distances

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach 1

[0029] Specific embodiment one: a kind of fingerprint map matching method based on Euclidean distance of this embodiment is realized according to the following steps:

[0030] Step 1: n APs evenly distributed in the area to be measured, n APs cover the entire area, use the receiver to sequentially measure the RSS vectors at m points to be measured, the points to be measured are evenly distributed in the entire area to be measured, and the measured The RSS vectors of m measurement points are stored in the fingerprint map, and the physical coordinates of the corresponding measurement points are recorded;

[0031] Where n is a positive integer greater than or equal to 3;

[0032] Step 2. Measure its RSS vector at the positioned point as (RSS 1 ,RSS 2 ,...,RSS n ), where RSS n is the signal strength of the nth AP;

[0033] Step 3: Calculate the Euclidean distance between the RSS vector measured by the positioned point in step 2 and the RSS vectors of m points stored in the fi...

specific Embodiment approach 2

[0044] Embodiment 2: This embodiment differs from Embodiment 1 in that the value of k is 3, 4, 5 or 6; other steps and parameters are the same as Embodiment 1.

specific Embodiment approach 3

[0045]Embodiment 3: This embodiment differs from Embodiment 1 or Embodiment 2 in that the optimum value of h obtained by simulation is h=3.

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 a fingerprint map matching method based on Euclidean distances, and relates to the technical field of fingerprint locating. The fingerprint map matching method based on the Euclidean distances aims to solve the problems that a traditional WKNN algorithm is low in locating accuracy, and the value of k can have large influences on locating results. The fingerprint map matching method based on the Euclidean distances comprises the first step of evenly distributing n receiving machines for AP in an area to be measured to measure the RSS vectors of m points to be measured, a second step of measuring the RSS vectors as (RSS1, RSS2, ..., RSSn) of located points at the located points, a third step of calculating the Euclidean distances from the located points to the m points in the fingerprint map in sequence, a fourth step of ranking the Euclidean distances in the third three from small to larger, a fifth step of calculating the weighing coefficients ql of the front k points, and a sixth step of carrying out summing after weighing is carried out on the physical coordinates of the obtained first k points obtained in the fourth step by using corresponding weighting coefficients to obtain the physical coordinates of the located points. The fingerprint map matching method based on the Euclidean distances is applied to the fingerprint locating area.

Description

technical field [0001] The invention relates to a fingerprint map matching method of Euclidean distance, and relates to the technical field of fingerprint positioning. Background technique [0002] Fingerprint positioning technology uses the existing wireless local area network and is widely used in indoor positioning. Due to the complexity of the indoor environment, positioning methods that rely solely on trigonometric calculations are greatly restricted, and fingerprint positioning technology is considered to be the development direction of indoor positioning technology. In the fingerprint positioning technology, the receiver calculates its own position by comparing the measurement results of the received signal with the pre-stored fingerprints, and the fingerprints are obtained by point-by-point measurement of the selected measurement points during the system establishment process. out. WLANs based on the IEEE802.11 protocol are widely distributed, whether in parks, com...

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): H04W64/00
Inventor 孟维晓巩紫君韩帅邹德岳
Owner HARBIN INST OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products