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In this paper, we present an algorithm for multidimensional vector regression on data that are highly uncertain and nonlinear, and then apply it to the problem of indoor location estimation in a wireless local area network (WLAN). Our aim is to obtain an accurate mapping between the signal space and the physical space without requiring too much human calibration effort. This location estimation problem has traditionally been tackled through probabilistic models trained on manually labeled data, which are expensive to obtain. In contrast, our algorithm adopts kernel canonical correlation analysis (KCCA) to build a nonlinear mapping between the signal-vector space and the physical location space by transforming data in both spaces into their canonical features. This allows the pairwise similarity of samples in both spaces to be maximally correlated using kernels. We use a Gaussian kernel to adapt to the noisy characteristics of signal strengths and a Matern kernel to sense the changes in physical locations. By using real data collected in an 802.11 wireless LAN environment, we achieve accurate location estimation for pervasive computing while requiring a much smaller set of labeled training data than previous methods
Multidimensional systems, Pervasive computing, Kernel, Wireless LAN, Signal mapping, Humans, Calibration, Signal analysis, Algorithm design and analysis, Gaussian noise,pervasive computing., Location-dependent and sensitive, correlation and regression analysis
"Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing", IEEE Transactions on Knowledge & Data Engineering, vol. 18, no. , pp. 1181-1193, September 2006, doi:10.1109/TKDE.2006.145
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