Abstract
In recent years, position information has become a key feature to drive location and context aware services in mobile communication. Researchers from all over the world have proposed many solutions for indoor positioning over the past several years. However, due to weak signals, multipath or non-line-of-sight signal propagation, accurately and efficiently localizing targets in harsh indoor environments remains a challenging problem. To improve the performance in harsh environment with insufficient anchors, cooperative localization has emerged. In this paper, a novel cooperative localization algorithm, named area optimization and node selection based sum-product algorithm over a wireless network (AN-SPAWN), is described and analyzed. To alleviate the high computational complexity and build optimized cooperative cluster, a node selection method is designed for the cooperative localization algorithm. Numerical experiment results indicate that our proposed algorithm has a higher accuracy and is less impacted by NLOS errors than other conventional cooperative localization algorithms in the harsh indoor environments.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.