臺灣博碩士論文加值系統

無人機需要室內定位技術於室內進行充電或待命,且定位精準度需小於一公尺。
絕大多數常見的定位方法依賴影像辨識,感應器和低頻率無線技術,但它們常受制於
照明條件,累積漂移誤差,和無線電波之間的干擾等等不同限制。60 Ghz 的毫米波無
線通訊技術對於上述的種種限制有較強的抵抗能力,因此,有成為良好室內定位科技
的潛力。我們使用了商用 WiGig 產品(COTS)的 WiGig 設備進行了許多實驗以確認這項科技作為室內定位的潛力。特別的是,我們發現採集發送和接收端的波束扇形指
紋可以用來辨別設備所在的位置。我們期待這個基於波束扇形指紋的特徵可以被採用
成為新的室內定位方法。

Drones require an indoor positioning function to park for charging or standby, and it needs sub-meter accuracy. Most conventional positioning approaches rely on cameras, sensors and low-frequency wireless technologies. They may suffer from changes in lighting conditions, accumulated drift errors, and wireless interference, respectively. The 60GHz
millimeter wave wireless technology, which is resistant to them, can be a potential candidate for the indoor positioning. We conduct experiments using COTS (Commercial
Off-The-Shelf) WiGig devices to confirm this potential. Specifically, we discover that the transmit and receive beam sectors can be fingerprinted to identify device locations. We expect that this sector-based fingerprinting feature can be employed for a new indoor positioning approach.

1 Introduction
1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Related Work
5
2.1 Fingerprinting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Triangulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Methodology
3.1
9
Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
3.1.1 Diversity from TX beam sectors . . . . . . . . . . . . . . . . . . . . 10
3.1.2 Diversity from RX beam sectors . . . . . . . . . . . . . . . . . . . . 11
3.1.3 Fingerprints based on TX/RX beam sectors . . . . . . . . . . . . . 12
3.2 System architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3 Positioning algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.3.1 Feature similarity metrics . . . . . . . . . . . . . . . . . . . . . . . 17
3.3.2 Fingerprint with RSSI . . . . . . . . . . . . . . . . . . . . . . . . . 19
4 Evaluation
20
4.1 Experiment setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.2 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.3 Experiment 2: Evaluation of different metrics . . . . . . . . . . . . . . . . 21
4.4
4.3.1 Euclidean distance metrics . . . . . . . . . . . . . . . . . . . . . . . 21
4.3.2 Cosine similarity metrics . . . . . . . . . . . . . . . . . . . . . . . . 23
Experiment 3: Evaluation of fingerprint without RSSI . . . . . . . . . . . . 26
5 Conclusion
27
5.1 Conclusion remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Bibliography
29

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