Hybrid algorithm for indoor positioning using wireless LAN

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, we develop 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. In the absence of a training dataset, the hybrid method coincides with the PL method and as the spatial granularity of training dataset increases, the result of the algorithm approaches the result of the LF method. It balances flexibility and accuracy of the two traditional methods, makes intelligent use of missing values, produces error bounds, and can be made dynamic. We evaluate the performance of the algorithm by applying it to a real site and observe satisfactory positioning accuracy.