An area optimization based cooperative localization algorithm with node selection

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.