A Fast Radio Map Construction Method Merging Self-Adaptive Local Linear Embedding (LLE) and Graph-Based Label Propagation in WLAN Fingerprint Localization Systems.

Yepeng Ni, Jianping Chai, Yan Wang, Weidong Fang

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

Nonlinear Feature Extraction Through Manifold Learning in an Electronic Tongue Classification Task.Sensors (Basel, Switzerland) · 2020
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5 · Who and what money

Authors and funding

4 authors.

Yepeng NiSchool of Data Science and Media Intelligence, Communication University of China, No.1 Dingfuzhuang East Street, Chaoyang District, Beijing 100024, China.

Jianping ChaiSchool of Data Science and Media Intelligence, Communication University of China, No.1 Dingfuzhuang East Street, Chaoyang District, Beijing 100024, China.

Yan WangSchool of Data Science and Media Intelligence, Communication University of China, No.1 Dingfuzhuang East Street, Chaoyang District, Beijing 100024, China.

Weidong FangKey Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Micro-System and Information Technology, Chinese Academy of Sciences, Shanghai 201800, China.

Funding

National Key R&D Program of China 2018YFB1404000

6 · The paper itself

Abstract

Indoor WLAN fingerprint localization systems have been widely applied due to the simplicity of implementation on various mobile devices, including smartphones. However, collecting received signal strength indication (RSSI) samples for the fingerprint database, named a radio map, is significantly labor-intensive and time-consuming. To solve the problem, this paper proposes a semi-supervised self-adaptive local linear embedding algorithm to build the radio map. First, this method uses the self-adaptive local linear embedding (SLLE) algorithm based on manifold learning to reduce the dimension of the high-dimensional RSSI samples and to extract a neighbor weight matrix. Secondly, a graph-based label propagation (GLP) algorithm is employed to build the radio map by semi-supervised learning from a large number of unlabeled RSSI samples to a few labeled RSSI samples. Finally, we propose a

Indexed as

graph-based label propagationindoor positioningLLEmanifold learningradio map

Identifiers

PMID32019229

DOI10.3390/s20030767

PMCPMC7038483

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.