Improved Indoor Tracking Based on Generalized t-Distribution Noise Model

The use of wireless sensor networks for indoor localization application has emerged as a significant area of interest over the last decade, primarily motivated by its low cost and convenient deployment. The weighted centroid localization algorithm is a suitable positioning technique in a wireless sensor network due to its easy implementation. However, the performance of this method is easily affected by outliers and interference in the measurement of radio signal strength. In order to overcome this limitation, a more robust ARMA filter using generalized t-distribution noise model based on influence function approach is proposed. A hardware prototype was implemented to demonstrate that the ARMA filter could improve system performance, especially when dealing with the case of measurement outliers.