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Wireless Detection, Localization and Tracking of Unmanned Aerial Vehicles.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Wireless Detection, Localization and Tracking of Unmanned Aerial Vehicles./
作者:	Sinha, Priyanka.
面頁冊數:	1 online resource (144 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-09, Section: B.
Contained By:	Dissertations Abstracts International84-09B.
標題:	Propagation. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30283624click for full text (PQDT)
ISBN:	9798374425796

Wireless Detection, Localization and Tracking of Unmanned Aerial Vehicles.
Sinha, Priyanka.

Wireless Detection, Localization and Tracking of Unmanned Aerial Vehicles. - 1 online resource (144 pages)

Source: Dissertations Abstracts International, Volume: 84-09, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2022.

Includes bibliographical references

Unmanned aerial systems (UAS) are on the verge of revolutionizing the state of the art air-transportation systems by providing services to people and places that have remained unreachable due to unavoidable circumstances. While a UAS can be used for numerous benevolent and economic applications, such as expedited dispatch of aid during the times of crisis, increasing the efficiency of various commercial processes, re-reinforcing surveillance, and potentially acting as a cellular HotSpot, certain use cases of UAS also impose security threats when in the hands of a malicious entity. Thus the ability to detect, localize, track a UAS and maintain communication among multiple travelling UAS become the building blocks of safely and smoothly integrating UAS in our lives. In this dissertation we identify the main problems faced by or faced due to wireless applications associated with a UAS. We then present strategies and algorithms to overcome these problems. In particular we address the challenges in detection, localization and tracking of UASs by wireless methods.Chapter-1 presents the motivation behind and organization of this dissertation. In Chapter-2, we develop an analytical framework that provides the fundamental limits on the network-wide UAS detection probability. In particular, we characterize the joint impact of the salient features of the terrestrial RF networks, such as the spatial randomness of the node locations, the directional 3D antenna patterns, and the mixed line of sight/non line of sight (LoS/NLoS) propagation characteristics of the air-to-ground (A2G) channels. Since the strength of the UAS signal and the aggregate interference in a sensing network are fundamentally limited by the 3D network geometry and the inherent spatial randomness, we use tools from stochastic geometry to derive the closed-form expressions for the probabilities of detection, false alarm and coverage. This, in turn, demonstrates the impact of the sensor density, beam tilt angle, half power beam width (HPBW) and different degrees of LoS dominance, on the projected detection performance. Our analysis reveals optimal beam tilt angles, and sensor density that maximize the network-wide detection of the UASs.In Chapter-3, we consider a scenario where a fixed number of radio-frequency (RF) sensors equipped with single or multiple dipole antennas are placed at some known locations on the ground, and they collect time-difference-of-arrival (TDOA) measurements from the UAS that is also equipped with a dipole antenna. We then use these measurements to estimate the 3D location of the UAS, and to derive the Cramer-Rao lower bounds (CRLBs) on the localization error for various orientations of the dipole antennas at the transmitter and the receiver. Namely, we consider vertical-vertical (VV), horizontal-horizontal (HH), and vertical-horizontal (VH) radiation patterns in a purely line-of-sight (LoS) environment and a mixed LoS/Non-line-of-sight (NLoS) environment. We show that the localization accuracy changes in a non-monotonic pattern with respect to the UAS altitude and identify the respective critical altitudes for each of the VV, VH and HH orientations. Subsequently, we propose a multi-antenna signal acquisition technique that mitigates the accuracy degradation due to the antenna pattern mismatches, and we derive the localization CRLB for the multi-antenna scenario. Our numerical results characterize achievable localization accuracy for various antenna configurations, UAS heights, and propagation conditions for representative UAS scenarios.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798374425796Subjects--Topical Terms:

3680519
Propagation.
Index Terms--Genre/Form:

542853
Electronic books.

Wireless Detection, Localization and Tracking of Unmanned Aerial Vehicles.
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502 $a Thesis (Ph.D.)--North Carolina State University, 2022.
504 $a Includes bibliographical references
520 $a Unmanned aerial systems (UAS) are on the verge of revolutionizing the state of the art air-transportation systems by providing services to people and places that have remained unreachable due to unavoidable circumstances. While a UAS can be used for numerous benevolent and economic applications, such as expedited dispatch of aid during the times of crisis, increasing the efficiency of various commercial processes, re-reinforcing surveillance, and potentially acting as a cellular HotSpot, certain use cases of UAS also impose security threats when in the hands of a malicious entity. Thus the ability to detect, localize, track a UAS and maintain communication among multiple travelling UAS become the building blocks of safely and smoothly integrating UAS in our lives. In this dissertation we identify the main problems faced by or faced due to wireless applications associated with a UAS. We then present strategies and algorithms to overcome these problems. In particular we address the challenges in detection, localization and tracking of UASs by wireless methods.Chapter-1 presents the motivation behind and organization of this dissertation. In Chapter-2, we develop an analytical framework that provides the fundamental limits on the network-wide UAS detection probability. In particular, we characterize the joint impact of the salient features of the terrestrial RF networks, such as the spatial randomness of the node locations, the directional 3D antenna patterns, and the mixed line of sight/non line of sight (LoS/NLoS) propagation characteristics of the air-to-ground (A2G) channels. Since the strength of the UAS signal and the aggregate interference in a sensing network are fundamentally limited by the 3D network geometry and the inherent spatial randomness, we use tools from stochastic geometry to derive the closed-form expressions for the probabilities of detection, false alarm and coverage. This, in turn, demonstrates the impact of the sensor density, beam tilt angle, half power beam width (HPBW) and different degrees of LoS dominance, on the projected detection performance. Our analysis reveals optimal beam tilt angles, and sensor density that maximize the network-wide detection of the UASs.In Chapter-3, we consider a scenario where a fixed number of radio-frequency (RF) sensors equipped with single or multiple dipole antennas are placed at some known locations on the ground, and they collect time-difference-of-arrival (TDOA) measurements from the UAS that is also equipped with a dipole antenna. We then use these measurements to estimate the 3D location of the UAS, and to derive the Cramer-Rao lower bounds (CRLBs) on the localization error for various orientations of the dipole antennas at the transmitter and the receiver. Namely, we consider vertical-vertical (VV), horizontal-horizontal (HH), and vertical-horizontal (VH) radiation patterns in a purely line-of-sight (LoS) environment and a mixed LoS/Non-line-of-sight (NLoS) environment. We show that the localization accuracy changes in a non-monotonic pattern with respect to the UAS altitude and identify the respective critical altitudes for each of the VV, VH and HH orientations. Subsequently, we propose a multi-antenna signal acquisition technique that mitigates the accuracy degradation due to the antenna pattern mismatches, and we derive the localization CRLB for the multi-antenna scenario. Our numerical results characterize achievable localization accuracy for various antenna configurations, UAS heights, and propagation conditions for representative UAS scenarios.
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650 4 $a Unmanned aerial vehicles. $3 3560267
650 4 $a Connectivity. $3 3560754
650 4 $a Drones. $3 3549538
650 4 $a Algorithms. $3 536374
650 4 $a Localization. $3 3560711
650 4 $a Radiation. $3 673904
650 4 $a Vehicles. $3 2145288
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Engineering. $3 586835
650 4 $a Robotics. $3 519753
650 4 $a Transportation. $3 555912
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