BigLoc: A Two-Stage Positioning Method for Large Indoor Space | Zendy

Zengwei Zheng | Zendy; Yuanyi Chen | Zendy; Sig Chen | Zendy; Lin Sun | Zendy; Dan Chen | Zendy

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BigLoc: A Two-Stage Positioning Method for Large Indoor Space

Author(s) -

Zengwei Zheng,

Yuanyi Chen,

Sig Chen,

Lin Sun,

Dan Chen

Publication year - 2016

Publication title -

international journal of distributed sensor networks

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.324

H-Index - 53

eISSN - 1550-1477

pISSN - 1550-1329

DOI - 10.1155/2016/1289013

Subject(s) - computer science , fingerprint (computing) , rss , variance (accounting) , signal strength , metric (unit) , fingerprint recognition , similarity (geometry) , signal (programming language) , artificial intelligence , pattern recognition (psychology) , space (punctuation) , feature vector , received signal strength indication , divergence (linguistics) , data mining , computer vision , wireless , telecommunications , image (mathematics) , operations management , accounting , economics , business , programming language , operating system , linguistics , philosophy

With the rapid development of WLAN infrastructure, fingerprint-based positioning using signal strength has become a promising localization solution in indoor space. Commonly fingerprint-based positioning methods face two challenges in large indoor space, one is floor recognition in large building with multifloor, and the other is signal strength variance due to heterogeneous devices and environmental factors. In this paper, we propose a novel two-stage positioning approach to address these challenges of fingerprint-based positioning methods in large indoor space. Firstly, we design a floor-level recognition feature based on WiFi access points and the RSS values to recognize floor. For solving the signal strength variance problem, we propose a new metric to capture the similarity of location fingerprints probability distribution using KL Divergence. To demonstrate the utility of our approach, we have performed comprehensive experiments in a large indoor building.

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