Probability Weighting Localization Algorithm Based on NLOS Identification in Wireless Network

Author:

Wu Shixun1ORCID,Zhang Shengjun1,Xu Kai1,Huang Darong1

Affiliation:

1. Information Science and Engineering College, Chongqing Jiaotong University, No. 66 Xuefu Road, Nan’an Dist, Chongqing 400074, China

Abstract

In this paper, a localization scenario that the home base station (BS) measures time of arrival (TOA) and angle of arrival (AOA) while the neighboring BSs only measure TOA is investigated. In order to reduce the effect of non-line of sight (NLOS) propagation, the probability weighting localization algorithm based on NLOS identification is proposed. The proposed algorithm divides these range and angle measurements into different combinations. For each combination, a statistic whose distribution is chi-square in LOS propagation is constructed, and the corresponding theoretic threshold is derived to identify each combination whether it is LOS or NLOS propagation. Further, if those combinations are decided as LOS propagation, the corresponding probabilities are derived to weigh the accepted combinations. Simulation results demonstrate that our proposed algorithm can provide better performance than conventional algorithms in different NLOS environments. In addition, computational complexity of our proposed algorithm is analyzed and compared.

Funder

China Scholarship Council

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A mobile localization algorithm based on fuzzy estimation for serious NLOS scenes;Peer-to-Peer Networking and Applications;2023-07-20

2. Two Efficient and Easy-to-Use NLOS Mitigation Solutions to Indoor 3-D AOA-Based Localization;2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN);2021-11-29

3. A Robust Localization Algorithm Based on NLOS Identification and Classification Filtering for Wireless Sensor Network;Sensors;2020-11-19

4. Learning to fuse local geometric features for 3D rigid data matching;Information Fusion;2020-09

5. Automatic ID Photos Matting Based on Improved CNN;2019 18th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES);2019-11

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