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2007

Adaptive Localization in a Dynamic WiFi Environment through Multi-view Learning

11 years 1 months ago
Adaptive Localization in a Dynamic WiFi Environment through Multi-view Learning
Accurately locating users in a wireless environment is an important task for many pervasive computing and AI applications, such as activity recognition. In a WiFi environment, a mobile device can be localized using signals received from various transmitters, such as access points (APs). Most localization approaches build a map between the signal space and the physical location space in a offline phase, and then using the received-signal-strength (RSS) map to estimate the location in an online phase. However, the map can be outdated when the signal-strength values change with time due to environmental dynamics. It is infeasible or expensive to repeat data calibration for reconstructing the RSS map. In such a case, it is important to adapt the model learnt in one time period to another time period without too much recalibration. In this paper, we present a location-estimation approach based on Manifold co-Regularization, which is a machine learning technique for building a mapping func...
Sinno Jialin Pan, James T. Kwok, Qiang Yang, Jeffr
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2007
Where AAAI
Authors Sinno Jialin Pan, James T. Kwok, Qiang Yang, Jeffrey Junfeng Pan
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