[1]HE Fugui,YANG Zheng,WU Chenshu,et al.An fingerprint calibrations-free indoor localization method based on hierarchical Levenshtein distance[J].CAAI Transactions on Intelligent Systems,2017,12(3):422-429.[doi:10.11992/tis.201704031]
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An fingerprint calibrations-free indoor localization method based on hierarchical Levenshtein distance

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 3 Page number: 422-429 Column: 学术论文—智能系统 Public date: 2017-06-25
Title:
An fingerprint calibrations-free indoor localization method based on hierarchical Levenshtein distance
Author(s):
HE Fugui1; 3;  YANG Zheng2;  WU Chenshu2;  ZHAO Shu3;  ZHOU Xiancun1
1. School of Electronics and Information Engineering, West Anhui University, Lu’an 237012, China;
2. Institute of Trustworthy Network and System, School of Software, Tsinghua University, Beijing 100084, China;
3. Institute of Intelligent Computing and Knowledge Engineering, Anhui University, Hefei 230039, China
Keywords:
indoor localization; WiFi fingerprint; heterogeneous device; fingerprint calibration-free; Levenshtein distance
CLC:
TP181
DOI:
10.11992/tis.201704031
Abstract:
In the era of mobile computing, location-based services have become extremely important for a wide range of applications, and various wireless indoor localization techniques have been emerging. Amongst these techniques, WiFi fingerprint-based indoor localization is one of the most attractive because of the wide deployment and availability of WiFi infrastructure. The accuracy of indoor localization is affected by two main factors: equipment heterogeneity and environmental dynamics. To solve the obove two problems, an algorithm based on hierarchical Levenshtein distance (HLD) was proposed to realize calibration-free fingerprint comparison of heterogeneous devices. Received signal strength indication(RSSI) information collected via different mobile devices was transformed into an AP sequence. The difference in the Received signal strength indication RSSI values was used to calculate the hierarchical energy level of each access point(AP). Next, the distance between the WiFi fingerprints was calculated using the Levenshtein distance. To locate WiFi fingerprint RSSI information, the HLD algorithm was used to obtain K neighbors and the weighted K nearest neighbor(WKNN) algorithm was used to predict its position. Five different mobile devices were used to collect WiFi RSSI information in three different types of indoor environments to verify the robustness and effectiveness of the algorithm. The average localization accuracy was 1.5 m.
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