Localization and Tracking of Objects Using Cross-Correlation of Shadow Fading Noise

Multipath and shadow fading are the primary cause for positioning errors in a Received Signal Strength Indicator (RSSI) based localization scheme. While fading, in general, is detrimental to localization accuracy, cross-correlation and divergence properties of shadow fading residuals may be utilized to improve localization and tracking accuracy of mobile IEEE 802.15.4 transmitters. Therefore, this paper begins by presenting a stochastic filter that models the fast changing multipath fading as a mean reverting Ornstein-Uhlenbeck (OU) process followed by a Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) filtering to isolate the slow changing shadow fading residuals from measured RSSI values. Subsequently, a novel wireless transmitter localization scheme that combines the measured cross-correlation in shadow fading residuals between adjacent receivers using a Student-t Copula likelihood function is proposed. However, the long convergence time for this highly non-convex copula function might render our method unsuitable for tracking applications. Therefore, we present a faster tracking method where the velocity and heading of a mobile transmitter are estimated from a-Divergence between shadow fading signals and an onboard gyroscope respectively. To bind the localization error in this tracking method, the transmitter location estimates are smoothed by a Bayesian particle filter. The performance of our proposed localization and tracking method is validated over simulations and hardware experiments.