This paper investigates a combination of fingerprinting (FP) and extended Kalman filter (EKF) based tracking aiming to tackle conventional problems related to implementation of either tracking or fingerprinting separately. One of the common drawbacks of FP belongs to large data size and consequent large search space. By taking advantage of latest position estimate got from EKF, a virtual surveillance area (VSA) is defined around the estimate. The dimension of this defined surveillance area is much smaller than the size of indoor environment. Consequently, there will be a possibility for FP to be applied in larger areas maintaining the possibility of adding necessary grid points in order to achieve a desired localization performance. Additionally, in order to improve accuracy of ranging, we investigate the impact of a priori knowledge related to the clusters impulse responses and other features; the applied so called soft ranging algorithm for time of arrival (TOA) estimation is modified in order to take advantage of this a priori information and to make its decision variables more accurate. Simulation results show a promising performance improvement via using the proposed hybrid tracking technique and applying a priori information to soft ranging. The tradeoff is along a reasonable increased implementation complexity.
Bybordi and Reggiani REF proposed a tracking algorithm which was a combination of fingerprinting and extended Kalman filter.
Hybrid Fingerprinting-EKF Based Tracking Schemes for Indoor Passive Localization
International Journal of Distributed Sensor Networks
Computer Science