Offline fingerprint database construction method, position fingerprint positioning method and system

A construction method and fingerprint library technology, applied in offline fingerprint library construction method, position fingerprint positioning method and system field, can solve the problems of increasing fingerprint matching calculation delay, etc., to reduce transmission delay, improve positioning accuracy, and high security Effect

Pending Publication Date: 2021-06-25
NANJING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention aims at the technical problem that the current fingerprint positioning method brings huge pressure on the cloud server to store fingerprint data, and also increases the calculation time delay of fingerprint matching, and provides an offline fingerprint database construction method, a location fingerprint positioning method and a system

Method used

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  • Offline fingerprint database construction method, position fingerprint positioning method and system
  • Offline fingerprint database construction method, position fingerprint positioning method and system
  • Offline fingerprint database construction method, position fingerprint positioning method and system

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Experimental program
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Effect test

Embodiment 1

[0060] Embodiment 1: a method for constructing an offline fingerprint library, including: in the offline stage, before the mobile device end transmits a plurality of sampling values ​​at the current reference point to the MEC server, it first completes the split operation of the fingerprint data through a clustering algorithm, That is, the reference point sampling data is transmitted to different MEC servers according to the clustering rules. The construction principle of the offline fingerprint library is as follows: image 3 As shown, the number of sub-databases of the fingerprint database is c, and the number of reference points of each sub-database is {n 1 ,n 2 ,...,n c}and

[0061] In the positioning model after the clustering operation is added, a new clustering operation is added to the task list of the MEC server to complete the classification and storage of fingerprint data. The specific process of building an offline fingerprint database is as follows:

[0062]...

Embodiment 2

[0076] Implementation 2: On the basis of Embodiment 1, this embodiment adopts the K-means clustering algorithm to obtain the clustering center of the sub-fingerprint library. K-means clustering, that is, K-means clustering. In the location fingerprint, the K-means algorithm uses the Euclidean distance as the standard of similarity, and divides the reference points into k classes. The similarity of reference points of different classes is low. . Assume that there are m reference points in the current entire area, respectively {L 1 , L 2 ,...,L m}, the number of APs used to create the fingerprint offline fingerprint library is n, respectively {AP 1 ,AP 2 ,...,AP n}. Reference point L i The coordinates are (xi,y i ), and its corresponding fingerprint is RSS i ={rss 1,i ,rss 2,i ,...,rss n,i}. The specific process of K-means clustering is as follows:

[0077]Input: the number of classes k (1

[0078] Output: k clusters a...

Embodiment 3

[0089] Implementation 3: a location fingerprint positioning method, including: adopting the off-line fingerprint library construction method described in the above embodiments to construct a sub-library of the off-line fingerprint library;

[0090] Each mobile edge computing server performs matching based on the RSS vector of the target point and its stored sub-database for constructing the offline fingerprint database, and weights the matching results of each mobile edge computing server to obtain the final estimated position of the target point. The specific processing logic is as follows:

[0091] Input: Maximum weight threshold T high , the minimum weighted threshold T low .

[0092] Step1: After the preprocessing of the sampling data of the target point is completed, the RSS vector of the target point is R={rss 1 ,rss 2 ,...,rss n}, the RSS vector of the cluster center of the i (i=1,2,...,n) class is RSS c i ={rss c i,1 ,rss c i,2 ,...,rss c i,n}, the similar...

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Abstract

The invention provides an off-line fingerprint database construction method and a position fingerprint positioning method and system. The off-line fingerprint database construction method comprises the steps that mobile edge computing servers are deployed at all wireless access point devices respectively, and the method comprises the following steps: adopting a clustering algorithm for obtained fingerprint data to obtain a set number of clusters and clustering centers; and storing the data in each category into a corresponding mobile edge computing server according to the position of the initial clustering center to construct sub-libraries of the offline fingerprint libraries. According to the method, the MEC server is deployed at the AP equipment, preprocessing, storage and matching tasks of the fingerprint data are settled to the MEC server to be completed, and the MEC server is very close to a user, so that the transmission time delay of the fingerprint data can be greatly reduced. By using the MEC server, the storage and calculation pressure of the cloud server and the mobile terminal can be relieved.

Description

technical field [0001] The invention belongs to the technical field of positioning, and relates to a position fingerprint positioning method, in particular to an offline fingerprint database construction method, a position fingerprint positioning method and a system. Background technique [0002] With the maturity of wireless network technology, location awareness has become one of its more important services, and indoor positioning technology plays an increasingly important role in our daily life. The technology of using wireless signals to achieve indoor positioning is becoming more and more mature. The existing algorithms are roughly divided into two types: one is to estimate the distance through parameters, and the common parameter types are time of arrival (TOA), time difference of arrival (TDOA), Angle of Arrival (AOA), etc.; the other is to use location fingerprints for positioning, and location fingerprints refer to wireless signal power values ​​that can reflect the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W4/02H04W4/33H04W4/021H04W64/00H04L29/08G06K9/62
CPCH04W4/023H04W4/33H04W4/021H04W64/006H04L67/10G06F18/23213
Inventor 陆音石陈杰杨楚瀛李清远
Owner NANJING UNIV OF POSTS & TELECOMM
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