Optimization of UWB indoor positioning based on hardware accelerated Fuzzy ISODATA

被引：2

作者：

Guo, Hua ^{[1
]}

Song, Shanshan ^{[1
]}

Yin, Haozhou ^{[1
]}

Ren, Daokuan ^{[1
]}

Zhu, Xiuwei ^{[1
]}

机构：

[1] Shandong Univ Sci & Technol, Coll Elect & Informat Engn, Qingdao 266590, Peoples R China

来源：

SCIENTIFIC REPORTS | 2024年 / 14卷 / 01期

关键词：

Indoor positioning; UWB; NLOS errors; Fuzzy ISODATA; FPGA acceleration;

D O I：

10.1038/s41598-024-68998-0

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

With the development of wireless communication technology, Ultra-Wideband (UWB) has become an important solution for indoor positioning. In complex indoor environments, the influence of non-line-of-sight (NLOS) factors leads to increased positioning errors. To improve the positioning accuracy, fuzzy iterative self-organizing data analysis clustering algorithm (ISODATA) is introduced to process a large amount of UWB data to reduce the influence of NLOS factors, and to stabilize positioning error within 2 cm, enhances the accuracy of the positioning system. To further improve the running efficiency of the algorithm, FPGA is used to accelerate the key computational part of the algorithm, compared with running on the MATLAB platform, which improves the speed about 100 times, enhances the algorithm's computational speed dramatically.

引用

页数：15

共 21 条

[1] Toward Multi-FPGA Acceleration of the Neural Networks [J].

Biookaghazadeh, Saman ;

Ravi, Pravin Kumar ;

Zhao, Ming .

ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS, 2021, 17 (02)

[2] Channel-Quality-Evaluation-Based Anchor Node Selection for UWB Indoor Positioning [J].

Chen, Chunxue ;

Huang, Zheng ;

Wang, Jiayu ;

Yuan, Lei ;

Bao, Jun ;

Chen, Zhuming .

ELECTRONICS, 2022, 11 (03)

[3] UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis [J].

Guo, Hua ;

Li, Mengqi ;

Zhang, Xuejing ;

Gao, Xiaotian ;

Liu, Qian .

FRONTIERS IN NEUROROBOTICS, 2022, 16

[4]

Hao Z., 2018, Math. Probl. Eng, V2018, P1

[5]

Hua Guo, 2020, 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), P778, DOI 10.1109/ICPICS50287.2020.9202123

[6] UWB NLOS/LOS Classification Using Deep Learning Method [J].

Jiang, Changhui ;

Shen, Jichun ;

Chen, Shuai ;

Chen, Yuwei ;

Liu, Di ;

Bo, Yuming .

IEEE COMMUNICATIONS LETTERS, 2020, 24 (10) :2226-2230

[7] Deep Learning for Counting People from UWB Channel Impulse Response Signals [J].

Lee, Gun ;

An, Subin ;

Jang, Byung-Jun ;

Lee, Soochahn .

SENSORS, 2023, 23 (16)

[8] On Indoor Localization Using WiFi, BLE, UWB, and IMU Technologies [J].

Leitch, Samuel G. ;

Ahmed, Qasim Zeeshan ;

Abbas, Waqas Bin ;

Hafeez, Maryam ;

Laziridis, Pavlos I. ;

Sureephong, Pradorn ;

Alade, Temitope .

SENSORS, 2023, 23 (20)

[9]

Li C., 2021, Acad. J. Eng. Technol. Sci, V4, P81

[10] Transfer learning-based UWB indoor localization using MHT-MDC and clusterization-based sparse fingerprinting [J].

Morawska, Barbara ;

Lipinski, Piotr ;

Lichy, Krzysztof ;

Adamkiewicz, Krzysztof .

JOURNAL OF COMPUTATIONAL SCIENCE, 2022, 61

← 1 2 3 →