Abstract
Mobile localization estimation is a significant research topic in the fields of wireless sensor network (WSN), which is of concern greatly in the past decades. Non-line-of-sight (NLOS) propagation seriously decreases the positioning accuracy if it is not considered when the mobile localization algorithm is designed. NLOS propagation has been a serious challenge. This paper presents a novel mobile localization method in order to overcome the effects of NLOS errors by utilizing the mean shift-based Kalman filter. The binary hypothesis is firstly carried out to detect the measurements which contain the NLOS errors. For NLOS propagation condition, mean shift algorithm is utilized to evaluate the means of the NLOS measurements and the data association method is proposed to mitigate the NLOS errors. Simulation results show that the proposed method can provide higher location accuracy in comparison with some traditional methods.
Highlights
Wireless localization is one of the technologies in the fields of the intelligent robot, national security, and health surveillance [1] and has received the researcher’s considerable attention in the past decades
This paper presents an efficient mobile node localization approach which is termed as improved Kalman filter (IKF) based on mean shift [27] to overcome the NLOS effect in mixed LOS/NLOS environments
We investigated the mobile localization in rough environments and presented a novel IKF algorithm which can realize the accurate mobile node localization
Summary
Wireless localization is one of the technologies in the fields of the intelligent robot, national security, and health surveillance [1] and has received the researcher’s considerable attention in the past decades. GPS is not able to provide the desirable performance when the receiver is in indoor environments. In the WSN-based localization approaches’ design, the location of the beacon nodes and the measurements between the beacon nodes and unknown node are assumed to be the known prior information. The measurement contains a positive bias which is termed as NLOS error. In this environment, the performance of the conventional positioning methods will degrade dramatically. The accurate localization in the NLOS environments has been a significant topic. We propose a novel location algorithm which can solve the NLOS errors.
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