Switching Extended Kalman Filter Bank for Indoor Localization Using Wireless Sensor Networks

This paper presents a new filtering algorithm, switching extended Kalman filter bank (SEKFB), for indoor localization using wireless sensor networks. SEKFB overcomes the problem of uncertain process-noise covariance that arises when constant-velocity motion model localization. In several filters (EKFs) run in parallel set hypotheses, and most probable output obtained from EKFs is selected Mahalanobis distance evaluation. Simulations demonstrated can provide accurate reliable without careful selection covariance.