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AAAI
2006

A Manifold Regularization Approach to Calibration Reduction for Sensor-Network Based Tracking

13 years 5 months ago
A Manifold Regularization Approach to Calibration Reduction for Sensor-Network Based Tracking
The ability to accurately detect the location of a mobile node in a sensor network is important for many artificial intelligence (AI) tasks that range from robotics to context-aware computing. Many previous approaches to the location-estimation problem assume the availability of calibrated data. However, to obtain such data requires great effort. In this paper, we present a manifold regularization approach known as LeMan to calibration-effort reduction for tracking a mobile node in a wireless sensor network. We compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled data and a large amount of unlabeled data. This mapping function can be used online to determine the location of mobile nodes in a sensor network based on the signals received. We use Crossbow MICA2 to setup the network and USB camera array to obtain the ground truth. Experimental results show that we can achieve a higher accuracy with much less calibration eff...
Jeffrey Junfeng Pan, Qiang Yang, Hong Chang, Dit-Y
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where AAAI
Authors Jeffrey Junfeng Pan, Qiang Yang, Hong Chang, Dit-Yan Yeung
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