An Indoor Robust Localization Algorithm Based on Data Association Technique

Abstract

As a key technology of the Internet of Things, wireless sensor network (WSN) has been used widely in indoor localization systems. However, when the sensor is transmitting signals, it is affected by the non-line-of-sight (NLOS) transmission, and the accuracy of the positioning result is decreased. Therefore, solving the problem of NLOS positioning has become a major focus for indoor positioning. This paper focuses on solving the problem of NLOS transmission that reduces positioning accuracy in indoor positioning. We divided the anchor nodes into several groups and obtained the position information of the target node for each group through the maximum likelihood estimation (MLE). By identifying the NLOS method, a part of the position estimates polluted by NLOS transmission was discarded. For the position estimates that passed the hypothesis testing, a corresponding poly-probability matrix was established, and the probability of each position estimate from line-of-sight (LOS) and NLOS was calculated. The position of the target was obtained by combining the probability with the position estimate. In addition, we also considered the case where there was no continuous position estimation through hypothesis testing and through the NLOS tracking method to avoid positioning errors. Simulation and experimental results show that the algorithm proposed has higher positioning accuracy and higher robustness than other algorithms.

Recent Trends on IoT Systems for Traffic Monitoring and for Autonomous and Connected Vehicles

“…The special issue is characterized by 11 original research papers [1][2][3][4][5][6][7][8][9][10][11]. The first paper [1], "Towards an End-to-End Framework of CCTV-Based Urban Traffic Volume Detection and Prediction", is written by M. V. Peppa, T. Komar, Wen Xiao, P. James, C. Robson, Jin Xing, and S. Barr from the University of Newcastle, UK; and the University of Melbourne, Australia.…”

“…The special issue is characterized by 11 original research papers [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ].…”

“…The third paper [ 3 ], “An Indoor Robust Localization Algorithm Based on Data Association Technique”, is written by Long Cheng, Yong Wang, Mingkun Xue, and Yangyang Bi. The authors are from Northeastern University, China and SANY Group, China.…”

NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation

“…Improving the robustness of UWB localization using outlier detection methods has been proposed and validated in NLOS conditions [ 13 ]. For localization in wireless sensor networks, NLOS identification and localization algorithms based on the residual analysis [ 14 ] or data association [ 15 ] have been proposed. The localization method was proposed using the multipath fingerprints produced by ray tracing and machine learning [ 16 ] or constrained L1-norm minimization method [ 17 ].…”

“…Even if the experiment was conducted, there are some limitations, e.g., a map should be given [ 5 ], the locations where UWB anchors are installed should be given [ 12 , 17 ], and it works only in a limited area [ 13 ]. The positioning algorithm using NLOS identification is a useful technique in wireless sensor networks [ 14 , 15 ]. However, a large number of network APs (access points) are required to cover large areas.…”