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Zhang J, Yi Q, Huang L, Yang Z, Cheng J, Zhang H. Research on None-Line-of-Sight/Line-of-Sight Identification Method Based on Convolutional Neural Network-Channel Attention Module. SENSORS (BASEL, SWITZERLAND) 2023; 23:8552. [PMID: 37896642 PMCID: PMC10611321 DOI: 10.3390/s23208552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/11/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023]
Abstract
None-Line-of-Sight (NLOS) propagation of Ultra-Wideband (UWB) signals leads to a decrease in the reliability of positioning accuracy. Therefore, it is essential to identify the channel environment prior to localization to preserve the high-accuracy Line-of-Sight (LOS) ranging results and correct or reject the NLOS ranging results with positive bias. Aiming at the problem of the low accuracy and poor generalization ability of NLOS/LOS identification methods based on Channel Impulse Response (CIR) at present, the multilayer Convolutional Neural Networks (CNN) combined with Channel Attention Module (CAM) for NLOS/LOS identification method is proposed. Firstly, the CAM is embedded in the multilayer CNN to extract the time-domain data features of the original CIR. Then, the global average pooling layer is used to replace the fully connected layer for feature integration and classification output. In addition, the public dataset from the European Horizon 2020 Programme project eWINE is used to perform comparative experiments with different structural models and different identification methods. The results show that the proposed CNN-CAM model has a LOS recall of 92.29%, NLOS recall of 87.71%, accuracy of 90.00%, and F1-score of 90.22%. Compared with the current relatively advanced technology, it has better performance advantages.
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Affiliation(s)
- Jingjing Zhang
- State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China; (J.Z.); (Q.Y.); (Z.Y.); (J.C.); (H.Z.)
- The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China
- School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
| | - Qingwu Yi
- State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China; (J.Z.); (Q.Y.); (Z.Y.); (J.C.); (H.Z.)
- The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China
- School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
| | - Lu Huang
- State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China; (J.Z.); (Q.Y.); (Z.Y.); (J.C.); (H.Z.)
- The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China
| | - Zihan Yang
- State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China; (J.Z.); (Q.Y.); (Z.Y.); (J.C.); (H.Z.)
- The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China
| | - Jianqiang Cheng
- State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China; (J.Z.); (Q.Y.); (Z.Y.); (J.C.); (H.Z.)
- The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China
| | - Heng Zhang
- State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China; (J.Z.); (Q.Y.); (Z.Y.); (J.C.); (H.Z.)
- The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China
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Xu S, Wang Y, Si M. A Two-Step Fusion Method of Wi-Fi FTM for Indoor Positioning. SENSORS (BASEL, SWITZERLAND) 2022; 22:3593. [PMID: 35591286 PMCID: PMC9102024 DOI: 10.3390/s22093593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 05/06/2022] [Accepted: 05/06/2022] [Indexed: 05/27/2023]
Abstract
The Wi-Fi fine time measurement (FTM) protocol specified in the IEEE 802.11-2016 standard provides a new two-way ranging approach to enhance positioning capability. Similar to other wireless signals, the accuracy of the real-time range measurement of FTM is influenced by various errors. In this work, the characteristics of the ranging errors is analyzed and an abstract ranging model is introduced. From the perspective of making full use of the range measurements from FTM, this paper designs two positioning steps and proposes a fusion method to refine the performance of indoor positioning. The first step is named single-point positioning, locating the position with the real-time range measurements based on the geometric principle. The second step is named the improved matching positioning, which constructs a distance database by utilizing the existing scene information and uses the modified matching algorithm to obtain the position. In view of the different positioning accuracies and error distributions from the results of the aforementioned two steps, a fusion method using the indirect adjustment principle is proposed to adjust the positioning results, and the advantages of the matching scene information and the range measurements are served simultaneously. Finally, a number of tests are conducted to assess the performance of the proposed method. The experimental results demonstrate that the precision and stability of indoor positioning are improved by the proposed fusion method.
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Affiliation(s)
- Shenglei Xu
- Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, China;
- School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;
| | - Yunjia Wang
- Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, China;
| | - Minghao Si
- School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;
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Kia G, Ruotsalainen L, Talvitie J. Toward Accurate Indoor Positioning: An RSS-Based Fusion of UWB and Machine-Learning-Enhanced WiFi. SENSORS 2022; 22:s22093204. [PMID: 35590894 PMCID: PMC9100004 DOI: 10.3390/s22093204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/14/2022] [Accepted: 04/18/2022] [Indexed: 02/05/2023]
Abstract
A wide variety of sensors and devices are used in indoor positioning scenarios to improve localization accuracy and overcome harsh radio propagation conditions. The availability of these individual sensors suggests the idea of sensor fusion to achieve a more accurate solution. This work aims to address, with the goal of improving localization accuracy, the fusion of two conventional candidates for indoor positioning scenarios: Ultra Wide Band (UWB) and Wireless Fidelity (WiFi). The proposed method consists of a Machine Learning (ML)-based enhancement of WiFi measurements, environment observation, and sensor fusion. In particular, the proposed algorithm takes advantage of Received Signal Strength (RSS) values to fuse range measurements utilizing a Gaussian Process (GP). The range values are calculated using the WiFi Round Trip Time (RTT) and UWB Two Way Ranging (TWR) methods. To evaluate the performance of the proposed method, trilateration is used for positioning. Furthermore, empirical range measurements are obtained to investigate and validate the proposed approach. The results prove that UWB and WiFi, working together, can compensate for each other’s limitations and, consequently, provide a more accurate position solution.
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Affiliation(s)
- Ghazaleh Kia
- Department of Computer Science, University of Helsinki, 00014 Helsinki, Finland;
- Correspondence:
| | - Laura Ruotsalainen
- Department of Computer Science, University of Helsinki, 00014 Helsinki, Finland;
| | - Jukka Talvitie
- Unit of Electrical Engineering, Tampere University, 33014 Tampere, Finland;
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Ando H, Sekoguchi S, Ikegami K, Yoshitake H, Baba H, Myojo T, Ogami A. Combining Indoor Positioning Using Wi-Fi Round Trip Time with Dust Measurement in the Field of Occupational Health. SENSORS 2021; 21:s21217261. [PMID: 34770567 PMCID: PMC8587963 DOI: 10.3390/s21217261] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/26/2021] [Accepted: 10/29/2021] [Indexed: 12/04/2022]
Abstract
Monitoring of personal exposure to hazardous substances has garnered increasing attention over the past few years. However, no straightforward and exact indoor positioning technique has been available until the recent discovery of Wi-Fi round trip time (Wi-Fi RTT). In this study, we investigated the possibility of using a combination of Wi-Fi RTT for indoor positioning and a wearable particle monitor (WPM) to observe dust concentration during walking in a simulated factory. Ultrasonic humidifiers were used to spray sodium chloride solution inside the factory. The measurements were recorded three times on different routes (Experiments A, B, and C). The error percentages, i.e., measurements that were outside the expected measurement area, were 7% (49 s/700 s) in Experiment A, 2.3% (15 s/660 s) in Experiment B, and 7.8% (50 s/645 s) in Experiment C. The dust measurements were also recorded without any obstruction. A heat map was created based on the results from both measured values. Wi-Fi RTT proved useful for computing the indoor position with high accuracy, suggesting the applicability of the proposed methodology for occupational health monitoring.
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Affiliation(s)
- Hajime Ando
- Department of Work Systems and Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555, Japan; (S.S.); (K.I.); (H.Y.); (H.B.); (A.O.)
- Correspondence:
| | - Shingo Sekoguchi
- Department of Work Systems and Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555, Japan; (S.S.); (K.I.); (H.Y.); (H.B.); (A.O.)
| | - Kazunori Ikegami
- Department of Work Systems and Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555, Japan; (S.S.); (K.I.); (H.Y.); (H.B.); (A.O.)
| | - Hidetaka Yoshitake
- Department of Work Systems and Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555, Japan; (S.S.); (K.I.); (H.Y.); (H.B.); (A.O.)
| | - Hiroka Baba
- Department of Work Systems and Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555, Japan; (S.S.); (K.I.); (H.Y.); (H.B.); (A.O.)
| | - Toshihiko Myojo
- Department of Environmental Health Engineering, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555, Japan;
| | - Akira Ogami
- Department of Work Systems and Health, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, 1-1, Iseigaoka, Yahatanishi-ku, Kitakyushu, Fukuoka 807-8555, Japan; (S.S.); (K.I.); (H.Y.); (H.B.); (A.O.)
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High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN. SENSORS 2021; 21:s21113701. [PMID: 34073449 PMCID: PMC8198425 DOI: 10.3390/s21113701] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/21/2021] [Accepted: 05/24/2021] [Indexed: 11/17/2022]
Abstract
Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.
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