空中無線感測：在無人機抖動環境下基於RSS的非接觸式人體偵測與定位

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：20

、訪客IP：3.141.6.23

姓名

陳維(Wei Chen) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

空中無線感測：在無人機抖動環境下基於RSS的非接觸式人體偵測與定位
(Sensing from the Sky: RSS-based Device-free Human Detection and Localization in the Presence of UAV Jittering)

相關論文

★ 基於多代理人強化學習方法多架無人機自主追蹤之研究	★ 連網無人機路徑規劃與基地台連線策略之共同設計：使用模仿增強的深度強化學習方法
★ 網路編碼於多架無人機網路以抵禦機器學習攻擊之研究	★ 使用多代理人強化學習於無線快取網路設計空中基地台三維路徑之研究
★ 基於 Swin-Transformer 語意分割的雷達訊號解交織之研究	★ 協作式自編碼器嵌入最佳化方法於無人機群網路以接收訊號強度輔助定位之研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2026-9-1以後開放)

摘要(中)

近年來，基於無線電的人體感測引起了大量的研究關注，並具有廣泛的應用，例如電子醫療監控、室內安全和工業監控。大多數現有研究都集中在分析固定接收器收集的無線信號擾動上。在本論文中，我們展示了 UH-Sense，這是第一個使用無人機進行人體偵測和定位的系統，其中安裝在無人機上的全向性天線被用於測量來自周圍 WiFi 基地台的信號強度。為了克服無人機引起的噪聲，我們提出了一種新的基於數據驅動之學習方法，用於在無法得知噪聲特徵的情況下對損壞數據進行去噪。接著，我們為無人機開發了基於無線電斷層成像的定位模型，無需收集指紋數據庫即可定位周圍的人。我們顯示 UH-Sense 可輕鬆實現於現代商用平台上，並評估其在不同實際環境中的性能，包括不規則基地台之部署和非可直視之場景。實驗結果顯示，UH-Sense 實現了高偵測性能，其人體偵測性能的平均 F1 分數為93\%，並且具有與使用固定接收器收集之乾淨數據相似甚至更好的定位性能，這是目前既有的去噪方法都無法實現的。

摘要(英)

Radio-based human sensing has attracted substantial research attention and enjoyed wide applications such as e-healthcare monitoring, indoor security, and industrial surveillance.
Most of the existing studies focus on capturing the perturbations of the wireless signals collected at a fixed receiver.
In this work, we present UH-Sense, the first unmanned aerial vehicle (UAV) based human detection and localization system in which an omnidirectional antenna mounted on the UAV is used to measure the signal strength from the surrounding WiFi access points (APs).
To overcome the multi-source UAV-induced noises, we propose a novel data-driven learning-based approach to denoise corrupted data without knowing the noise characteristics.
Then, a localization model based on radio tomography imaging (RTI) is developed for the UAV to localize surrounding human without collecting the fingerprint database.
We demonstrate that UH-Sense is readily deployable on commodity platforms and evaluate its performance in different real-world environments including irregular AP deployment and non-line-of-sight (NLOS) scenarios.
Experimental results suggest that UH-Sense achieves a high detection performance with an average F1 score of 0.93 and yields similar or even better localization performance than that of using clean data (i.e., data collected at a fixed receiver), which has not been achieved by any of the state-of-the-art denoising methods.

關鍵字(中)

★ 無線感測
★ 無設備定位
★ 機器學習
★ 無人機

關鍵字(英)

★ Wireless sensing
★ Device-free localization
★ Machine learning
★ Unmanned aerial vehicles

論文目次

論文摘要... i
Abstract... ii
目錄... v
圖目錄... vi
表目錄... viii
一、緒論... 1
1.1 研究背景... 1
1.2 研究動機與目的... 2
1.3 論文架構... 4
二、文獻探討... 5
2.1 基於RSS的人體感測... 5
2.1.1 固定接收器感測... 5
2.1.2 無人機感測... 6
2.2 降噪技術... 7
2.2.1 基於模型的方法... 7
2.2.2 資料驅動的方法... 7
三、系統概述... 9
3.1 問題闡述... 9
3.2 UH-Sense模組... 10
四、任務導向對抗性去噪自編碼器方法... 13
4.1 數據預處理... 13
4.2 數據去噪... 14
4.3 目標偵測... 18
4.4 位置估計... 19
4.5 UH-Sense架構... 20
五、實驗... 23
5.1 實驗平台... 23
5.2 實驗設置... 24
5.3 TO-ADAE設定... 27
5.4 比較方案... 28
六、實驗結果... 31
6.1 超參數之影響... 31
6.2 目標偵測準確率... 34
6.3 定位性能... 36
6.3.1 比較所有環境的平均RMSE... 36
6.3.2 評估不同環境下的影響... 40
七、結論與未來研究方向... 42
參考文獻... 43
附錄一... 52

參考文獻

[1] H. Zhu, F. Xiao, L. Sun, R. Wang, and P. Yang, “R-TTWD: Robust device-free through-the-wall detection of moving human with WiFi,” IEEE Journal on Selected Areas in ommunications, vol. 35, no. 5, pp. 1090–1103, 2017.
[2] H. Abdelnasser, K. Harras, and M. Youssef, “A ubiquitous WiFi-based fine-grained gesture recognition system,” IEEE ransactions on Mobile Computing, vol. 18, no. 11, pp. 2474–2487, 2019.
[3] D. Wu, D. Zhang, C. Xu, H. Wang, and X. Li, “Device-free WiFi human sensing: From pattern-based to model-based approaches,” IEEE
Communications Magazine, vol. 55, no. 10, pp. 91–97, 2017.
[4] J.Wang, H. Jiang, J. Xiong, K. Jamieson, X. Chen, D. Fang, and B. Xie, “LiFS: Low human-effort, device-free localization with fine-grained subcarrier information,” in Proceedings of the 22nd Annual International
Conference on Mobile Computing and Networking, 2016.
[5] S. Shi, S. Sigg, L. Chen, and Y. Ji, “Accurate location tracking from CSI-based passive device-free probabilistic fingerprinting,” IEEE Transactions on Vehicular Technology, vol. 67, no. 6, pp. 5217–5230, 2018.
[6] Y. Ma, B.Wang,W. Ning, and Y. Zhang, “PRSRTI: A novel device-free localization method using phase response shift based radio tomography imaging,” IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 13 812–13 820, 2020.
[7] X. Ding, T.-M. Choi, and Y. Tian, “HRI: Hierarchic radio imaging-based device-free localization,” IEEE Transactions on Systems, Man,
and Cybernetics: Systems, Early access, 2020.
[8] L. Chen, J. Xiong, X. Chen, S. I. Lee, K. Chen, D. Han, D. Fang, Z. Tang, and Z. Wang, “WideSee: Towards wide-area contactless wireless sensing,” in Proceedings of ACM SenSys, New York, NY, USA, 2019.
[9] Z. Yan, T. Duckett, and N. Bellotto, “Online learning for human classification in 3D LiDAR-based tracking,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017.
[10] B. Vandersmissen, N. Knudde, A. Jalalvand, I. Couckuyt, A. Bourdoux, W. De Neve, and T. Dhaene, “Indoor person identification using a lowpower FMCW radar,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 7, pp. 3941–3952, 2018.
[11] M. Banagar, H. S. Dhillon, and A. F. Molisch, “Impact of UAV wobbling on the air-to-ground wireless channel,” IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 14 025–14 030, 2020.
[12] W. Chen, D.-K. Chang, and Y.-J. Chen, “Combating the impact of jittering in UAV-based sensing systems using deep denoising network,” in IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), 2020.
[13] A. Buffi, A. Motroni, P. Nepa, B. Tellini, and R. Cioni, “A SAR-based measurement method for passive-tag positioning with a flying UHFRFID reader,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 3, pp. 845–853, 2019.
[14] C. Luo, P. Casaseca-de-la Higuera, S. McClean, G. Parr, and P. Ren, “Characterization of received signal strength perturbations using allan variance,” IEEE Transactions on Aerospace and Electronic Systems, vol. 54, no. 2, pp. 873–889, 2018.
[15] Y. Qu and H. Qi, “uDAS: An untied denoising autoencoder with sparsity for spectral unmixing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 3, pp. 1698–1712, 2019.
[16] S. Langarica and F. Nunez, “Dual blind denoising autoencoders for industrial process data filtering,” arXiv preprint rXiv:2004.06806, 2020.
[17] A. Shokry, M. M. Elhamshary, and M. Youssef, “DynamicSLAM: Leveraging human anchors for ubiquitous low-overhead indoor localization,” IEEE Transactions on Mobile Computing, Early access, 2020.
[18] S. Sorour, Y. Lostanlen, S. Valaee, and K. Majeed, “Joint indoor localization and radio map construction with limited deployment load,” IEEE Transactions on Mobile Computing, vol. 14, no. 5, pp. 1031– 1043, 2015.
[19] O. Kaltiokallio, R. Jäntti, and N. Patwari, “ARTI: An adaptive radio tomographic imaging system,” IEEE Transactions on Vehicular Technology, vol. 66, no. 8, pp. 7302–7316, 2017.
[20] O. Kaltiokallio, R. Hostettler, and N. Patwari, “A novel Bayesian filter for RSS-based device-free localization and tracking,” IEEE Transactions on Mobile Computing, vol. 20, no. 3, pp. 780–795, 2021.
[21] S. Depatla and Y. Mostofi, “Crowd counting through walls usingWiFi,” in IEEE International Conference on Pervasive Computing and Communications (PerCom), 2018.
[22] S. Depatla and Y. Mostofi, “Passive crowd speed estimation in adjacent regions with minimalWiFi sensing,” IEEE Transactions on Mobile Computing, vol. 19, no. 10, pp. 2429–2444, 2020.
[23] P. Hillyard and N. Patwari, “Never use labels: Signal strength-based Bayesian device-free localization in changing environments,” IEEE Transactions on Mobile Computing, vol. 19, no. 4, pp. 894–906, 2020.
[24] J. Wilson and N. Patwari, “See-through walls: Motion tracking using variance-based radio tomography networks,” IEEE Transactions on Mobile Computing, vol. 10, no. 5, pp. 612–621, 2011.
[25] Y. Ma, C. Tian, and H. Liu, “MUSE: A multistage assembling algorithm for simultaneous localization of large-scale massive passive,” IEEE Transactions on Mobile Computing, Early access, 2020.
[26] C. R. Karanam and Y. Mostofi, “3D through-wall imaging with unmanned aerial vehicles using WiFi,” in ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), 2017.
[27] D. Zhang, J. Wang, J. Jang, J. Zhang, and S. Kumar, “On the feasibility ofWiFi based material sensing,” in Annual International Conference on Mobile Computing and Networking, 2019.
[28] T. Zheng, Z. Chen, C. Cai, J. Luo, and X. Zhang, “V2iFi: In-vehicle vital sign monitoring via compact RF sensing,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 4, no. 2, 2020.
[29] M. Weiss, R. C. Paffenroth, J. R. Whitehill, and J. R. Uzarski, “Deep learning with domain randomization for optimal filtering,” in 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019.
[30] D. L. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613–627, 1995.
[31] J. Lázaro, N. Reljin, M. B. Hossain, Y. Noh, P. Laguna, and K. H. Chon, “Wearable armband device for daily life electrocardiogram monitoring,” IEEE Transactions on Biomedical Engineering, vol. 67, no. 12, pp. 3464–3473, 2020.
[32] K. Wang, X. Chen, L. Wu, X. Zhang, X. Chen, and Z. J. Wang, “Highdensity surface EMG denoising using independent vector analysis,”
IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 6, pp. 1271–1281, 2020.
[33] X. Gong, W. Chen, and J. Chen, “A low-rank tensor dictionary learning method for hyperspectral image denoising,” IEEE Transactions on Signal Processing, vol. 68, pp. 1168–1180, 2020.
[34] G. Jiang, P. Xie, H. He, and J. Yan, “Wind turbine fault detection using a denoising autoencoder with temporal information,” IEEE/ASME Transactions on Mechatronics, vol. 23, no. 1, pp. 89–100, 2018.
[35] S. Pascual, M. Park, J. Serrà, A. Bonafonte, and K. Ahn, “Language and noise transfer in speech enhancement generative adversarial network,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
[36] Y. Ma, B.Wang, X. Gao, andW. Ning, “The gray analysis and machine learning for device-free multitarget localization in passive UHF RFID environments,” IEEE Transactions on Industrial Informatics, vol. 16, no. 2, pp. 802–813, 2020.
[37] N. Patwari and P. Agrawal, “Effects of correlated shadowing: Connectivity, localization, and RF tomography,” in International Conference on Information Processing in Sensor Networks, 2008.
[38] A. F. Bastos, K. Lao, G. Todeschini, and S. Santoso, “Novel moving average filter for detecting RMS voltage step changes in triggerless PQ data,” IEEE Transactions on Power Delivery, vol. 33, no. 6, pp. 2920–2929, 2018.
[39] S. Pascual, A. Bonafonte, and J. Serra, “SEGAN: Speech enhancement generative adversarial network,” arXiv preprint arXiv:1703.09452, 2017.
[40] M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in Proceedings of the 34th International Conference on Machine Learning, 2017.
[41] S. Panwar, P. Rad, T. P. Jung, and Y. Huang, “Modeling EEG data distribution with a wasserstein generative adversarial network to predict RSVP events,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 8, pp. 1720–1730, 2020.
[42] C. Kim, S. Park, and H. J. Hwang, “Local stability of wasserstein GANs with abstract gradient penalty,” IEEE Transactions on Neural Networks and Learning Systems, Early access, 2021.
[43] J. Zheng, J.-C. Chen, V. M. Patel, C. D. Castillo, and R. Chellappa, “Hybrid dictionary learning and matching for video-based face verification,” in IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2019.
[44] Z.Wang, L. Qin, X. Guo, and G.Wang, “Dual-radio tomographic imaging with shadowing-measurement awareness,” IEEE Transactions on
Instrumentation and Measurement, vol. 69, no. 7, pp. 4453–4464, 2020.
[45] R. Li, W. Cao, S. Wu, and H. S. Wong, “Generating target image-label pairs for unsupervised domain adaptation,” IEEE Transactions on Image Processing, vol. 29, pp. 7997–8011, 2020.
[46] L. Meier, “Mavlink: Micro air vehicle communication protocol,” 2006. [Online]. Available: https://mavlink.io/en/
[47] 3DR, “Dronekit-python,” 2004. [Online]. Available: https://github.com/dronekit/dronekit-python
[48] W. Chen and Y. J. Chen, “Task-oriented adversarial denoising autoencoder,” 2021. [Online]. Available: https://github.com/ICANLab/
TOADAE.git
[49] H. Liu, J. Ma, T. Xu, W. Yan, L. Ma, and X. Zhang, “Vehicle detection and classification using distributed fiber optic acoustic sensing,” IEEE Transactions on Vehicular Technology, vol. 69, no. 2, pp. 1363–1374, 2020.
[50] Z. Hasiewicz, M. Pawlak, and P. Sliwinski, “Nonparametric identification of nonlinearities in block-oriented systems by orthogonal wavelets with compact support,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 52, no. 2, pp. 427–442, 2005.
[51] K. Tamura, R. Choi, and Y. Aoki, “Unconstrained and calibration-free gaze estimation in a room-scale area using a monocular camera,” IEEE Access, vol. 6, pp. 10 896–10 908, 2018.
[52] J. Dunn and R. Tron, “Temporal siamese networks for clutter mitigation applied to vision-based quadcopter formation control,” IEEE Robotics and Automation Letters, vol. 6, no. 1, pp. 32–39, 2021.

指導教授

陳昱嘉(Yu-Jia Chen)

審核日期

2021-12-23

推文