An algorithm for fast, model-free tracking indoors

Author:

Chen Aiyou1,Harko Cristina2,Lambert Diane3,Whiting Phil1

Affiliation:

1. Alcatel-Lucent, Murray Hill, NJ

2. Smith College, Northampton, MA

3. Google, New York, NY

Abstract

Tracking people and objects indoors from signal strength measurements has applications as diverse as security monitoring, self-guided museum tours, and personalization of communications services. Accurate dynamic tracking in real-time has been elusive, though, because signal propagation in buildings and the paths that people follow are complex. This paper proposes a simple algorithm for indoor tracking that requires neither a propagation model nor a motion model. It is also simple to compute, requiring only standard tools: Delaunay triangulation, linear interpolation, moving averaging, and local regression. Experiments with real data and simulations based on real data show that the algorithm is not only simple, it is effective.

Publisher

Association for Computing Machinery (ACM)

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Privacy-preserving crowdsourced site survey in WiFi fingerprint-based localization;EURASIP Journal on Wireless Communications and Networking;2016-05-04

2. Revisiting Gaussian Process Regression Modeling for Localization in Wireless Sensor Networks;Sensors;2015-09-08

3. Pedestrian Localization in Closed Environments;IFIP Advances in Information and Communication Technology;2013

4. Probability kernel regression for WiFi localisation;Journal of Location Based Services;2012-06

5. Geolocating for web based geospatial applications;Advances in Web-based GIS, Mapping Services and Applications;2011-05-10

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