Wireless LAN White Papers
Accurate and Low-Cost Location Estimation Using Kernels
Overview This paper presents a novel method for indoor-location estimation using a vector-space model based on signals received from a wireless client. It aims at obtaining an accurate mapping between the signal space and the physical space without incurring too much human calibration effort. This problem has traditionally been tackled through probabilistic models trained on manually labeled data, which are expensive to obtain. The paper presents a novel approach to building a mapping between the signal-vector space and the physical location space using Kernel Canonical Correlation Analysis (KCCA). The algorithm are tested in a 802.11 wireless LAN environment, and the advantage of the method is demonstrated in both accuracy and its ability to utilize a much smaller set of labeled training data than previous methods.
| Publisher | Hong Kong University of Science and Technology | File Format | |
|---|---|---|---|
| Date Published | July 2005 | ||
| Format | White Papers | ||
| Topics | |||
-
Did you find this white paper useful?
White Paper RSS
Download
Register
