Paper Info
Title
A Localization Based on Unscented Kalman Filter and Particle Filter Localization Algorithms.
Abstract
Localization plays an important role in the field of Wireless Sensor Networks (WSNs) and robotics. Currently, localization is a very vibrant scientific research field with many potential applications. Localization offers a variety of services for the customers, for example, in the field of WSN, its importance is unlimited, in the field of logistics, robotics, and IT services. Particularly localization is coupled with the case of human-machine interaction, autonomous systems, and the applications of augmented reality. Also, the collaboration of WSNs and distributed robotics has led to the creation of Mobile Sensor Networks (MSNs). Nowadays there has been an increasing interest in the creation of MSNs and they are the preferred aspect of WSNs in which mobility plays an important role while an application is going to execute. To overcome the issues regarding localization, the authors developed a framework of three algorithms named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Particle Filter (PF) Localization algorithms. In our previous study, the authors only focused on EKF-based localization. In this paper, the authors present a modified Kalman Filter (KF) for localization based on UKF and PF Localization. In the paper, all these algorithms are compared in very detail and evaluated based on their performance. The proposed localization algorithms can be applied to any type of localization approach, especially in the case of robot localization. Despite the harsh physical environment and several issues during localization, the result shows an outstanding localization performance within a limited time. The robustness of the proposed algorithms is verified through numerical simulations. The simulation results show that proposed localization algorithms can be used for various purposes such as target tracking, robot localization, and can improve the performance of localization.
Year
DOI
Venue
2020
10.1109/ACCESS.2019.2961740
IEEE ACCESS
Keywords
Field
DocType
Extended Kalman filter,localization,particle filter,robot,unscented Kalman filter,wireless sensor networks
Extended Kalman filter,Computer science,Particle filter,Algorithm,Augmented reality,Robustness (computer science),Kalman filter,Artificial intelligence,Monte Carlo localization,Wireless sensor network,Robotics
Journal
Volume
ISSN
Citations 
8
2169-3536
4
PageRank 
References 
Authors
0.43
0
5
Name
Order
Citations
PageRank
Inam Ullah140.76
Yu Shen240.43
Xin Su3219.92
Christian Esposito456954.78
Chang Choi526139.04