Square-Root Unscented Kalman Filtering Based Localization and Tracking in the Internet of Things

Target localization and tracking in the Internet of Things (IoT) environment have been paid more and more attention recently. The knowledge and information generated from wireless sensor nodes of the IoT make huge contributions to localization and tracking of targets with high mobility. This paper presents a square-root unscented Kalman filtering (SR-UKF) based localization and tracking algorithm for mobile target in an IoT environment. First, a localization initialization model is proposed for an IoT scenario. Then, according to information of neighboring sensor nodes, we employ the SR-UKF idea for the further localization and tracking of the target. Simulation results demonstrate that the proposed algorithm achieves lower localization and tracking error under the same computational complexity, compared with some conventional extended Kalman filtering (EKF) or UKF based methods. The proposed algorithm is of great significance in the field of IoT information processing.