Handling Samples Correlation in the Horus System

Abstract

We present an autoregressive model for modeling samples autocorrelation from the same access point in WLAN location determination systems. Our work is in the context of the Horus system, which is a probabilistic WLAN location determination system. We show that the autocorrelation between consecutive samples from the same access point can be as high as 0.9. Using our model, we describe a technique to use multiple signal strength samples from each access point, taking the high autocorrelation into account, to achieve better accuracy. Implementation of the technique in the Horus system shows that the average system accuracy is increased by more than 50%. Our results show that assuming independence of samples from the same access point can lead to degraded performance as the number of samples used in the estimation algorithm is increased, due to the wrong independence assumption. We also discuss how to incorporate the new technique with other algorithms for enhancing the performance of WLAN location determination systems. (UMIACS-TR-2003-75)