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

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陳維(Wei Chen) 查詢紙本館藏

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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)

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★ 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

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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

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指導教授

陳昱嘉(Yu-Jia Chen)

審核日期

2021-12-23

推文