Gaussian Filtered RSSI-based Indoor Localization in WLAN using Bootstrap Filter

被引:2
作者
Wang, Jingjing [1 ]
Hwang, Jun Gyu [1 ]
Peng, Jishen [1 ]
Park, Jaewoo [1 ]
Park, Joon Goo [1 ]
机构
[1] Kyungpook Natl Univ, Elect & Elect Engn, Daegu, South Korea
来源
2021 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATION (ICEIC) | 2021年
关键词
Indoor Positioning Algorithm; Received signal strength Indicator; Bootstrap Filter;
D O I
10.1109/CEIC51217.2021.9369804
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The ranging technology based on Received Signal Strength Index (RSSI) is widely used in Wireless Local Area Network (WLAN) positioning technology due to its low cost and low complexity. In the indoor positioning algorithm of RSSI positioning technology, due to the complexity of indoor environment and the randomness of personnel and other factors, it may be affected by noise, which needs to be suppressed. Based on the analysis and research of RSSI value, a processing algorithm of signal attenuation model combining Gaussian filter and Bootstrap filter is proposed. In the experiment, Gaussian filter is used to filter the abnormal RSSI value to get the optimal value, and then the nonlinear signal attenuation model is processed by Bootstrap filter algorithm. The experiment was carried out in a representative indoor environment and an anechoic chamber. Compared with the existing ranging algorithm based on average RSSI value, the algorithm can effectively remove the mutation data and noise fluctuation in RSSI value, realize the accurate smooth output of RSSI value and establish an accurate ranging model.
引用
收藏
页数:4
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[21]   APPLYING HYBRID QUANTUM LSTM FOR INDOOR LOCALIZATION BASED ON RSSI [J].
Chien, S. F. ;
Chieng, David ;
Chen, Samuel Y. C. ;
Zarakovitis, Charilaos C. ;
Lim, H. S. ;
Xu, Y. H. .
2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, ICASSP 2024, 2024, :131-135
[22]   Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview [J].
Singh, Navneet ;
Choe, Sangho ;
Punmiya, Rajiv .
IEEE ACCESS, 2021, 9 :127150-127174
[23]   RSSI-based localisation algorithms using spatial diversity in wireless sensor networks [J].
Hamdoun, Safa ;
Rachedi, Abderrezak ;
Benslimane, Abderrahim .
INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING, 2015, 19 (3-4) :157-167
[24]   Improvement of Adaptability to RSSI-based Positioning System using Scaling Circle Method [J].
Yang, Qizheng ;
Fukunaga, Hayato ;
Li, Tong ;
Tateno, Shigeyuki .
2022 61ST ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS (SICE), 2022, :264-269
[25]   RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization [J].
Hu, Weiwei ;
Pokkuluri, Kiran Sree ;
Arunachalam, Rajesh ;
Jabr, Bander A. ;
Ali, Yasser A. ;
Palanisamy, Preethi .
CMC-COMPUTERS MATERIALS & CONTINUA, 2024, 81 (03) :4833-4851
[27]   Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms [J].
M. W. P. Maduranga ;
Valmik Tilwari ;
Ruvan Abeysekera .
Journal of Electrical Systems and Information Technology, 11 (1)
[28]   An Efficient Algorithm for Localization Using RSSI Based on ZigBee [J].
Zhang, Xuechao ;
Shrestha, Ravi ;
Wahid, Khan .
2015 IEEE 28TH CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE), 2015, :366-369
[29]   Extreme Learning Machine for Accurate Indoor Localization Using RSSI Fingerprints in Multifloor Environments [J].
Yan, Jun ;
Qi, Guowen ;
Kang, Bin ;
Wu, Xiaohuan ;
Liu, Huaping .
IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (19) :14623-14637
[30]   Improved hybrid algorithm of indoor wireless localization based on RSSI for wireless sensor networks [J].
Zhu, Jun ;
Gao, Hui ;
Ma, Shuai .
Journal of Computational Information Systems, 2013, 9 (09) :3707-3714