Wi-Fi RSS Indoor Positioning System Using Online Layer Clustering and Weighted DCP-KNN

AuthorsMohsen Borhani
Conference Title26th Iranian Conference on Electrical Engineering (ICEE2018)
Holding Date of Conference2018-05-08 - 2018-05-10
Event Place1 - مشهد
Presented byصنعتی سجاد
PresentationSPEECH
Conference LevelInternational Conferences

Abstract

K-nearest neighbors (KNN) methods can be used on indoor positioning system (IPS) based on Wi-Fi fingerprint in the context of internet of things. The positioning of a mobile device (MD) using Wi-Fi technology involves online and offline phases. In this paper, the offline phase includes data collection in WiFi-based Nonintrusive SMS (WinSMS) context, while the online phase involves updating the structure of the collected radio map and online positioning. In online positioning, the proposed Weighted Differential Coordinate Probabilistic-KNN (WDCP-KNN) method based on probabilistic weighting of generalized Reference Points (RPs) and differential coordinates is used. Experiments in a complex indoor environment with real values indicate that the proposed method reduces the positioning error compared to other methods, and is also comparable in terms of computational complexity.

Paper URL

tags: Radio map; RSS; Wi-Fi fingerprint; KNN methodes; Indoor Positioning