| Paper: | MLSP-P3.3 | ||
| Session: | Pattern Recognition | ||
| Time: | Wednesday, May 17, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Other Applications | ||
| Title: | Location Tracking in Wireless Local Area Networks with Adaptive Radio Maps | ||
| Authors: | Azadeh Kushki, Konstantinos Plataniotis, Anastasios Venetsanopoulos, University of Toronto, Canada | ||
| Abstract: | This paper proposes a dynamic MMSE estimator for tracking mobile users in indoor Wireless Local Area Networks (WLAN) based on Received Signal Strength (RSS). The method uses a training-based static estimate obtained by an adaptive kernel density estimator as the input into a Kalman Filter. Predictions from the filter are used during the next iteration to adaptively select a subset of training data, contained in a radio map, for the static estimator. Experimental results show that the combination of the Kalman filter and the adaptive radio map technique results in nearly 0.5m (20%) improvement in Root Mean Square location accuracy when compared to static localization. | ||
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