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Indoor positioning method based on spectrum regression kernel discriminant analysis for terminal building

A discriminant analysis and indoor positioning technology, applied in the aviation field, can solve the problems of low positioning efficiency, no consideration of RSS data interference, and increased positioning errors

Inactive Publication Date: 2018-11-27
CIVIL AVIATION UNIV OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] To sum up, the disadvantages of the traditional technology are: the traditional technology does not consider the interference of the RSS data when positioning. People have an impact on RSS data, this positioning method will increase the positioning error
And the traditional positioning method has the defect of low positioning efficiency

Method used

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  • Indoor positioning method based on spectrum regression kernel discriminant analysis for terminal building
  • Indoor positioning method based on spectrum regression kernel discriminant analysis for terminal building
  • Indoor positioning method based on spectrum regression kernel discriminant analysis for terminal building

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Experimental program
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Effect test

Embodiment 1

[0040] In order to verify the effect of the present invention, experimental verification is carried out according to the steps mentioned above. Experimental verification results such as Figure 2-4 shown.

[0041] Step 1: Offline data collection. The data collection experiment was carried out at the departure floor of Terminal T2 of Tianjin Binhai International Airport. Taking many factors into consideration, a rest area on the departure floor of the terminal building was selected as the experimental area. Judging from the collected data, in this rest area, multiple data collections at the same location fluctuate greatly due to the impact of people walking. The received signal strength data collected in this environment can effectively illustrate the advantages of the algorithm in this paper. Compared with most current indoor positioning, it is more practical. Take a reference node every 1m, a total of 10 reference nodes, as RP; Also select 10 locations in the experimental...

Embodiment 2

[0047] like Figure 5 As shown, this embodiment is to verify the impact of the number of offline samples on the data processing time, and its main implementation process includes:

[0048] In this embodiment, the King data set is used for verification. This data set is a relatively authoritative data set. Experiments on this data set can better explain the advantages and disadvantages of the algorithm. At the same time, the data set is also collected in a real environment, so the data is more descriptive. The data set includes 14,300 training sets and 5,060 test sets. Using the SRKDA algorithm on this data set can better reflect its computational superiority and greatly save calculation time.

[0049] This embodiment mainly focuses on the impact of different offline sample sizes on data processing time. In the process of offline sample processing, by controlling the number of offline samples from 1100 to 14300, other variables are kept constant.

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Abstract

The invention discloses an indoor positioning method based on spectrum regression kernel discriminant analysis (SRKDA) for a terminal building and belongs to the technical field of aviation. On the basis of analyzing characteristics of a terminal building positioning environment of an airport, the kernel method is used for processing data to improve the positioning precision. The method mainly comprises the following steps: step 1, acquiring intensity data of a receiving signal of a known position at an offline state; step 2, extracting nonlinear characteristics of fingerprints at an originalposition by utilizing a spectrum regression kernel discriminant analysis method, so as to generate a new characteristic fingerprint database; step 3, processing RSS (Root-Sum-Squares) data of a pointto be positioned at an online state by utilizing the SRKDA; step 4, carrying out position estimation by utilizing a KNN (K Nearest Neighbor) algorithm. The indoor positioning method disclosed by the invention can be widely applied to the terminal building of the airport; the indoor positioning precision can be effectively improved; meanwhile, the data processing time is effectively saved and the positioning efficiency is improved.

Description

technical field [0001] The invention belongs to the field of aviation technology, in particular to an indoor positioning method of a terminal building based on spectral regression kernel discriminant analysis. In view of the characteristics of large passenger flow and complex indoor environment in the airport terminal, the method involved in the present invention can perform spectrum regression kernel discriminant analysis on the original fingerprint data and store it in the characteristic fingerprint database, and perform the same processing on the fingerprint data in the online stage , and then use the processed data to match with the feature fingerprint database. The present invention can effectively improve indoor positioning accuracy, effectively save data processing time, and improve positioning efficiency. Background technique [0002] The airport terminal building is a large-scale key place for the distribution and interaction of civil aviation business such as chec...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W4/021H04W4/33H04W64/00
CPCH04W4/021H04W4/33H04W64/00
Inventor 丁建立穆涛王怀超
Owner CIVIL AVIATION UNIV OF CHINA