Sensors, Vol. 18, Pages 1722: Indoor Pedestrian Localization Using iBeacon and Improved Kalman Filter

Sensors, Vol. 18, Pages 1722: Indoor Pedestrian Localization Using iBeacon and Improved Kalman Filter Sensors doi: 10.3390/s18061722 Authors: Kwangjae Sung Dong Kyu ‘Roy’ Lee Hwangnam Kim The reliable and accurate indoor pedestrian positioning is one of the biggest challenges for location-based systems and applications. Most pedestrian positioning systems have drift error and large bias due to low-cost inertial sensors and random motions of human being, as well as unpredictable and time-varying radio-frequency (RF) signals used for position determination. To solve this problem, many indoor positioning approaches that integrate the user’s motion estimated by dead reckoning (DR) method and the location data obtained by RSS fingerprinting through Bayesian filter, such as the Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), have recently been proposed to achieve higher positioning accuracy in indoor environments. Among Bayesian filtering methods, PF is the most popular integrating approach and can provide the best localization performance. However, since PF uses a large number of particles for the high performance, it can lead to considerable computational cost. This paper presents an indoor positioning system implemented on a smartphone, which uses simple dead reckoning (DR), RSS fingerprinting using iBeacon and machine learning scheme, and improved KF. The core of the system is the enhanced KF called a sigma-point Kalman...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research