High-accuracy localization for indoor group users based on extended Kalman filter

Abstract

Indoor localization has attracted increasing research attentions in the recent years. However, many important issues still need to be further studied to keep pace with new requirements and technical progress, such as real-time operation, high accuracy, and energy efficiency. In order to meet the high localization accuracy requirement and the high localization dependable requirement in some scenarios, we take the users as a group to utilize the mutual distance information among them to get better localization performance. Moreover, we design a mobile group localization method based on extended kalman filter and believable factor of non-localized nodes, which can alleviate the influence caused by environmental noisy and unstable wireless signals to improve the localization accuracy. Besides, we implement a real system based on ZigBee technique and perform experiments on the campus of Huaqiao University. Experimental results and theoretical analysis validate the effectiveness of the proposed method.