| Publisher | University of California | ||
|---|---|---|---|
| Format | 216.7KB PDF | Date added | 02 Jul 2004 |
| Topics | Local Area Networks (LAN), Wireless LAN, Software Engineering | ||
| Downloads | 1 | ||
Locating an indoor mobile station based on wireless communication infrastructure, has practical applications. The most widely employed methods today use a RF propagation loss (PL) model or Location Fingerprinting (LF). The PL method is known to perform poorly compared to LF. But LF requires an extensive training dataset and cannot adapt well to configuration changes or receiver breakdown. In the current paper, one develops a hybrid method that combines the strength of these two methods. It first formulates the RF propagation loss in a nonlinear, censored regression model and adjusts the regression function to the observed signal strength in the fingerprint dataset.
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