臺灣博碩士論文加值系統

隨著無人機系統（UAS）越來越融入日常生活，人們對系統安全性的擔憂也隨之出現。 與空中交通管制 (ATC) 管理的有人飛機一樣，無人機運作也需要無人機交通管理 (UTM)，專為傳統 ATC 無法滿足的極低高度 (VLL) 量身定制。 研究目標以利用機器學習技術分析Remote ID網路在UTM飛行監控和避免碰撞的實際運用情形，包括在城市與農村地區以無人機執行小規模的飛行測試數據。
研究方法包括四個步驟。 第一步驟進行了全面的文獻綜述，以深入了解 UTM 通訊系統。 第二步驟透過小型固定翼無人機的飛行測試來評估Remote ID 應用的 4G 訊號品質。 第三步驟開發了基於Remote ID網路的UTM監控系統，並實現了在2D空間中使用倒置淚滴形狀的碰撞檢測演算法。 第四步驟將上一步擴展到使用固定翼無人機的 3D 衝突偵測演算法。
結果表明，Remote ID網路 透過現有 4G 蜂巢式網路有效支援 UTM 飛行監控。 用於碰撞檢測的新穎倒置淚滴形狀增強了無人機系統的操作合規性。 事實證明，機器學習技術在各種 UTM 操作中具有重要意義，例如預測 4G 訊號品質以及識別影響延遲時間和偵測警告的關鍵因素。 總而言之，這項研究為小型 UAS 操作的 UTM 飛行監控和衝突消除中Remote ID 的實施提供了寶貴的見解。

As unmanned aerial systems (UAS) become more integrated into daily life, concerns about safety and security have arisen. Like manned aircraft managed by air traffic control (ATC), UAS operations require UAS traffic management (UTM), tailored to very low altitudes (VLL) where traditional ATC falls short. The research objective is to analyze the implementation of network Remote ID in UTM flight monitoring and deconfliction using machine learning techniques based on flight test data for small UAS operations in urban and rural areas.
The research methodology consisted of four steps. The first research conducted a comprehensive literature review to gain insights into UTM communication systems. The second research aimed to assess the 4G signal quality for network Remote ID applications through flight tests with a small fixed-wing UAV. In the third research, a UTM monitoring system based on network Remote ID was developed, and a collision detection algorithm using an inverted teardrop shape in 2D space was implemented. The fourth research expanded the previous step into a 3D conflict detection algorithm using fixed-wing UAVs.
The results demonstrate that network Remote ID effectively supports UTM Flight Monitoring via existing 4G cellular networks. The novel inverted teardrop shape for conflict detection enhances UAS operational compliance. Machine learning techniques prove significant in various UTM operations such as the prediction of 4G signal quality and the identification of key factors influencing latency time and detection warnings. In conclusion, this research contributes valuable insights into the implementation of network Remote ID in UTM flight monitoring and deconfliction for small UAS operations.

摘要......i
Abstract......iii
Acknowledgments......iv
Table of Contents......v
List of Tables......ix
List of Figures......x
List of Abbreviation......xv
Symbols......xix
Chapter 1 Introduction......1
1.1 Background......1
1.2 Challenges......5
1.3 Research Objective and Questions......6
1.4 Research Scope......8
1.5 Main Contributions......9
1.5.1 UTM Multi-channel Communication Framework......9
1.5.2 4G Signal Quality to Support Network Remote ID......10
1.5.3 Real-time UTM Flight Monitoring based on Network Remote ID......10
1.5.4 Inverted Teardrop Shape Area in UAV Tactical Conflict Detection......11
1.5.5 Machine Learning Techniques in UTM Data Analysis......12
1.6 Dissertation Outline......13
Chapter 2 Literature Review......15
2.1 Introduction......15
2.2 Review Methodology......16
2.2.1 Review Design......17
2.2.2 Review Conduct......17
2.2.3 Qualitative Analysis......17
2.2.4 Review Writing Structure......19
2.3 Result of Review......19
2.3.1 Definition......19
2.3.2 Data Format......21
2.3.3 Technology......23
2.3.4 Research Applications......25
2.3.5 Remaining Issues......36
2.4 Discussion......39
2.4.1 SWOT Analysis......39
2.4.2 UTM Multi-channels Communication......40
2.5 Summary......42
Chapter 3 Background Information......44
3.1 Introduction......44
3.2 Remote ID......44
3.2.1 Concept of Remote ID......44
3.2.2 Standardization of Network Remote ID......46
3.2.3 Regulation of Network Remote ID......46
3.3 The 4G Connectivity Used in Network Remote ID......47
3.3.1 Concept of 4G LTE......48
3.3.2 Signal Quality Parameters......49
3.4 UTM Flight Monitoring......51
3.4.1 Conformance Monitoring......52
3.4.2 Airspace......52
3.4.3 UTM Volumes......53
3.5 Calculation of UAV Position......55
3.5.1 Pseudorange Calculation......56
3.5.2 Coordinate Transformation......58
3.5.3 Altitude Calculation......60
3.6 Summary......61
Chapter 4 Methodology......62
4.1 Introduction......62
4.2 Development of UTM Flight Monitoring......63
4.3 Inverted Teardrop Shape Conflict Detection......65
4.3.1 Two-Dimensional Inverted Teardrop Shape......66
4.3.2 Two-Dimensional Circle Shape......69
4.3.3 Near-Collision Area......71
4.3.4 Three-dimensional Inverted Teardrop Shape......72
4.3.5 User Interface of Inverted Teardrop Shape......75
4.4 Flight Test Arrangement......76
4.4.1 The Remote ID Hardware......76
4.4.2 The Small UAVs Model......78
4.4.3 Flight Test Locations......80
4.4.4 Flight Test Scenarios......80
4.4.5 Experimental Setup......83
4.5 Machine Learning Techniques for Data Analysis......84
4.5.1 Gaussian Process......85
4.5.2 Random Forest......86
4.5.3 Evaluation Metrices......87
4.6 Summary......88
Chapter 5 Result and Discussion......89
5.1 Introduction......89
5.2 Measurement of 4G Signal Quality for Network Remote ID......89
5.2.1 Flight Test Result......89
5.2.2 Statistical Analysis......92
5.2.3 Machine Learning Analysis......94
5.3 Evaluation of the UTM Flight Monitoring with 2D Conflict Detection......101
5.3.1 Flight Parameters Result......101
5.3.2 System Delay Analysis......103
5.3.3 Analysis of Detection Algorithm......106
5.4 Evaluation of the UTM Flight Monitoring with 3D Conflict Detection......108
5.4.1 Flight Test Records......108
5.4.2 Analysis of Latency Time......112
5.4.3 Analysis of Detection Warning......115
5.4.4 Analysis of Random Forest......117
5.5 Discussion......122
5.5.1 UTM Communication......122
5.5.2 The 4G Signal Quality for Network Remote ID......123
5.5.3 UTM Flight Monitoring based on Network Remote ID......124
5.5.4 Conflict Detection based on Inverted Teardrop Shape......125
5.5.5 Machine Learning in UTM Flight Monitoring system......125
5.6 Summary......126
Chapter 6 Conclusion and Recommendation......127
6.1 Conclusion......127
6.2 Recommendation......129
Reference......132
Appendix I......145
Appendix II......147
Appendix III......149
Extended Abstract......160

[1]M. S. Baum, Unmanned Aircraft Systems Traffic Management: UTM. 2022.
[2]EUROCONTROL, “UAS ATM Integration Operational Concept,” 2018.
[3]Ministry of Civil Aviation of India, “National Unmanned Aircraft System Traffic Management (UTM) Policy Framework,” 2021.
[4]Federal Aviation Administration, “Integration of civil unmanned aircraft systems (UAS) in the national airspace system (NAS) roadmap,” 2013.
[5]J. Rios, D. Mulfinger, J. Homola, and P. Venkatesan, “NASA UAS Traffic Management National Campaign: Operations across Six UAS Test Sites,” in IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), 2016, pp. 1–6.
[6]A. S. Aweiss, B. D. Owens, J. L. Rios, J. R. Homola, and C. P. Mohlenbrink, “Unmanned Aircraft Systems (UAS) Traffic Management (UTM) National Campaign II,” AIAA Inf. Syst. Infotech@ Aerosp., pp. 1–16, 2018.
[7]A. Aweiss et al., “Flight Demonstration of Unmanned Aircraft System (UAS) Traffic Management (UTM) at Technical Capability Level 3,” AIAA/IEEE Digit. Avion. Syst. Conf. - Proc., vol. 2019-Septe, 2019, doi: 10.1109/DASC43569.2019.9081718.
[8]E. Murrell, Z. Walker, E. King, and K. Namuduri, “Remote ID and Vehicle-to-Vehicle Communications for Unmanned Aircraft System Traffic Management,” in International Workshop on Communication Technologies for Vehicles, 2020, pp. 194–202, doi: 10.1007/978-3-030-66030-7_17.
[9]S. Bradford and P. Kopardekar, “UTM Pilot Program (UPP) Summary Report,” 2019.
[10]S. Bradford and P. Kopardekar, “UTM Pilot Program (UPP) Phase 2 Final Report,” 2021.
[11]SESAR Joint Undertaking, “U-space Blueprint,” 2017. doi: 10.2829/614891.
[12]SESAR Joint Undertaking, “European ATM Master Plan Executive View,” 2020. doi: 10.2829/650097.
[13]Federal Aviation Administration, “UTM Concept of Operations v2.0,” 2020. doi: 10.1201/9781003124689.
[14]M. Sarim, M. Radmanesh, M. Dechering, M. Kumar, R. Pragada, and K. Cohen, “Distributed Detect-and-Avoid for Multiple Unmanned Aerial Vehicles in National Air Space,” J. Dyn. Syst. Meas. Control. Trans. ASME, vol. 141, no. 7, pp. 1–9, 2019, doi: 10.1115/1.4043190.
[15]X. Zhang, Y. Meng, C. Mao, Y. Xu, and N. Bai, “A Design of a Developable Automatic Avoidance System of UAV Based on ADS-B,” Wirel. Commun. Mob. Comput., vol. 2021, 2021, doi: 10.1155/2021/3072606.
[16]Drone Alliance Europe, “Drones, UTM and Spectrum – A Review,” 2016. [Online]. Available: http://dronealliance.eu/wp-content/uploads/2016/06/Spectrum-Allocation-White-Paper-Drone-Alliance-Europe-fin.pdf.
[17]C. Zhong et al., “A Cost-Benefit Analysis to Achieve Command and Control (C2) Link Connectivity for beyond Visual Line of Sight (BVLOS) Operations,” Integr. Commun. Navig. Surveill. Conf. ICNS, vol. 2020-Septe, pp. 1–14, 2020, doi: 10.1109/ICNS50378.2020.9222956.
[18]D. Constantine, “The Future of the Drone Economy,” 2020. https://levitatecap.com/levitate/wp-content/uploads/2020/12/White-Paper-v4.pdf (accessed Jul. 08, 2021).
[19]ResearchAndMarkets.com, “Global UAS Traffic Management (UTM) System Market 2021-2031: System Architecture, Use Cases, Enabling Technologies and Country-Wise UTM Concepts,” Business Wire, 2021. https://www.businesswire.com (accessed Jul. 08, 2021).
[20]H. Snyder, “Literature review as a research methodology: An overview and guidelines,” J. Bus. Res., vol. 104, no. July, pp. 333–339, 2019, doi: 10.1016/j.jbusres.2019.07.039.
[21]N. Ruseno, C. Lin, and S. Chang, “UAS Traffic Management Communications : The Legacy of ADS-B , New Establishment of Remote ID , or Leverage of ADS-B-Like Systems ?,” Drones, vol. 6, no. 57, pp. 1–21, 2022.
[22]A. Carrio, Y. Lin, S. Saripalli, and P. Campoy, “Obstacle Detection System for Small UAVs using ADS-B and Thermal Imaging,” J. Intell. Robot. Syst. Theory Appl., vol. 88, no. 2–4, pp. 583–595, 2017, doi: 10.1007/s10846-017-0529-2.
[23]Y. Kim, J. Y. Jo, and S. Lee, “ADS-B vulnerabilities and a security solution with a timestamp,” IEEE Aerosp. Electron. Syst. Mag., vol. 32, no. 11, pp. 52–61, 2017, doi: 10.1109/MAES.2018.160234.
[24]Y. Pan et al., “When UAVs coexist with manned airplanes: Large-scale aerial network management using ADS-B,” Trans. Emerg. Telecommun. Technol., vol. 30, no. 10, pp. 1–18, 2019, doi: 10.1002/ett.3714.
[25]Z. P. Languell and Q. Gu, “Securing ADS-B with multi-point distance-bounding for UAV collision avoidance,” IEEE 16th Int. Conf. Mob. Ad Hoc Sens. Syst., 2019.
[26]G. L. Orrell, A. Chen, and C. J. Reynolds, “Small unmanned aircraft system (SUAS) automatic dependent surveillance-broadcast (ADS-B) like surveillance concept of operations: A path forward for small UAS surveillance,” AIAA/IEEE Digit. Avion. Syst. Conf. - Proc., vol. 2017-Septe, 2017, doi: 10.1109/DASC.2017.8102026.
[27]M. M. Azari, G. Geraci, A. Garcia-Rodriguez, and S. Pollin, “Cellular UAV-to-UAV Communications,” IEEE Int. Symp. Pers. Indoor Mob. Radio Commun. PIMRC, vol. 2019-Septe, 2019, doi: 10.1109/PIMRC.2019.8904448.
[28]N. Hosseini, H. Jamal, J. Haque, T. Magesacher, and D. W. Matolak, “UAV Command and Control, Navigation and Surveillance: A Review of Potential 5G and Satellite Systems,” IEEE Aerosp. Conf. Proc., vol. 2019-March, no. March, pp. 1–10, 2019, doi: 10.1109/AERO.2019.8741719.
[29]F. Minucci, E. Vinogradov, and S. Pollin, “Avoiding Collisions at Any (Low) Cost: ADS-B like Position Broadcast for UAVs,” IEEE Access, vol. 8, pp. 121843–121857, 2020, doi: 10.1109/ACCESS.2020.3007315.
[30]A. Ghubaish, T. Salman, and R. Jain, “Experiments with a LoRaWAN-Based Remote ID System for Locating Unmanned Aerial Vehicles (UAVs),” Wirel. Commun. Mob. Comput., vol. 2019, 2019, doi: 10.1155/2019/9060121.
[31]D. Kubo, A. Osedo, and I. Yasui, “Low Altitude Situational Awareness Enhancement using Remote ID Broadcasted from small UAS,” in AIAAAviation 2020 Forum, 2020, vol. 1 Part F, pp. 6–13, doi: 10.2514/6.2020-2867.
[32]International Civil Aviation Organisation, “ADS-B Implementation and Operations Guidance Document,” vol. 13, no. September, 2020.
[33]C. E. Lin, “An ADS-B like communication for UTM,” Integr. Commun. Navig. Surveill. Conf. ICNS, vol. 2019-April, pp. 1–12, 2019, doi: 10.1109/ICNSURV.2019.8735199.
[34]Y. H. Lin, C. E. Lin, and H. C. Chen, “ADS-B Like UTM surveillance using APRS infrastructure,” Aerospace, vol. 7, no. 7, pp. 1–14, 2020, doi: 10.3390/AEROSPACE7070100.
[35]ASTM, Standard Specification for Remote ID and Tracking. 2019, pp. 1–67.
[36]A. K. Ishihara, J. Rios, and P. Venkatesan, “Remote id for rapid assessment of flight and vehicle information,” AIAA Scitech 2019 Forum, no. January, 2019, doi: 10.2514/6.2019-2077.
[37]M. O. Duffield and T. W. McLain, “A well clear recommendation for small UAS in high-density, ADS-B-enabled airspace,” AIAA Inf. Syst. Infotech Aerospace, 2017, no. January, 2017, doi: 10.2514/6.2017-0908.
[38]T. Sherman, T. Elemy, M. Retherford, T. Cady, and S. Bhandari, “Collision avoidance system for fixed-wing uavs using ping-2020 ads-b transceivers,” AIAA Scitech 2019 Forum, no. January 2019, pp. 1–9, 2019, doi: 10.2514/6.2019-2075.
[39]N. S. Curtis-Brown, I. A. Guzman, T. M. Sherman, J. Tellez, E. Gomez, and S. Bhandari, “UAV collision detection and avoidance using ADS-B sensor and custom ADS-B like solution,” AIAA Inf. Syst. Infotech Aerospace, 2017, no. January 2017, pp. 1–12, 2017, doi: 10.2514/6.2017-1153.
[40]M. Mozaffari, X. Lin, and S. Hayes, “Towards 6G with Connected Sky: UAVs and Beyond,” arXiv Prepr. arXiv2103.01143, 2021.
[41]X. M. Tang, J. Da Chen, and T. Li, “Unmanned aerial vehicle trajectory data fusion based on an active and passive feedback system,” Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng., vol. 234, no. 3, pp. 887–895, 2020, doi: 10.1177/0954410019890796.
[42]H. Zhang, Y. Yan, S. Li, Y. Hu, and H. Liu, “UAV Behavior-Intention Estimation Method Based on 4-D Flight-Trajectory Prediction,” Sustainability, 2021.
[43]C. W. Lum et al., “Uas operation and navigation in gps-denied environments using multilateration of aviation transponders,” AIAA Scitech 2019 Forum, no. January, 2019, doi: 10.2514/6.2019-1053.
[44]B. Duffy and L. Glaab, “Variable-power ADS-B for UAS,” AIAA/IEEE Digit. Avion. Syst. Conf. - Proc., vol. September, pp. 0–5, 2019, doi: 10.1109/DASC43569.2019.9081640.
[45]M. Palenska, S. L. Brazdilova, P. Casek, and L. Korenciak, “Low-Power ADS-B for GA Operating in Low Altitude Airspace,” AIAA/IEEE Digit. Avion. Syst. Conf. - Proc., vol. October, 2020, doi: 10.1109/DASC50938.2020.9256553.
[46]H. Sallouha, A. Chiumento, and S. Pollin, “Aerial Vehicles Tracking Using Noncoherent Crowdsourced Wireless Networks,” IEEE Trans. Veh. Technol., vol. 70, no. 10, pp. 10780–10791, 2021, doi: 10.1109/TVT.2021.3103315.
[47]A. Sidibe, G. Loubet, A. Takacs, G. Ferré, and A. Ghiotto, “Miniature drone antenna design for the detection of airliners,” Int. J. Microw. Wirel. Technol., vol. 13, no. 1, pp. 21–27, 2021, doi: 10.1017/S1759078720000896.
[48]J. Goossen, J. Kim, and S. Van Der Hoeven, “Development of a versatile ADS-B communications system,” Proc. 2018 Texas Symp. Wirel. Microw. Circuits Syst. WMCS 2018, pp. 1–4, 2018, doi: 10.1109/WMCaS.2018.8400647.
[49]Y. Zhu, X. Shi, and K. Kang, “UAV-Based Flight Inspection System,” in International Flight Inspection Symposium, 2018, pp. 16–20.
[50]V. Alarcón et al., “Procedures for the integration of drones into the airspace based on u-space services,” Aerospace, vol. 7, no. 9, pp. 1–18, 2020, doi: 10.3390/aerospace7090128.
[51]V. D. Berdonosov, A. A. Zivotova, Z. Htet Naing, and D. O. Zhuravlev, “Speed Approach for UAV Collision Avoidance,” J. Phys. Conf. Ser., vol. 1015, no. 5, 2018, doi: 10.1088/1742-6596/1015/5/052002.
[52]Z. P. Languell and Q. Gu, “A Multi-Point Distance-Bounding Protocol for Securing Automatic Dependent Surveillance-Broadcast in Unmanned Aerial Vehicle Applications,” J. Comput. Sci. Technol., vol. 35, no. 4, pp. 825–842, 2020, doi: 10.1007/s11390-020-0260-5.
[53]Z. Lee, R. Kumar, R. Radmanesh, M. Kumar, and K. Cohen, “Application of fuzzy logic for developing sense and avoid techniques for uav flight operations in national airspace,” ASME 2020 Dyn. Syst. Control Conf. DSCC 2020, vol. 2, no. October, 2020, doi: 10.1115/DSCC2020-3268.
[54]M. Radmanesh, M. Kumar, and M. Sarim, “Grey wolf optimization based sense and avoid algorithm in a Bayesian framework for multiple UAV path planning in an uncertain environment,” Aerosp. Sci. Technol., vol. 77, no. 2018, pp. 168–179, 2018, doi: 10.1016/j.ast.2018.02.031.
[55]P. Pierpaoli and A. Rahmani, “UAV collision avoidance exploitation for noncooperative trajectory modification,” Aerosp. Sci. Technol., vol. 73, pp. 173–183, 2018, doi: 10.1016/j.ast.2017.12.008.
[56]V. D. Berdonosov, A. A. Zivotova, D. O. Zhuravlev, and Z. H. Naing, “Implementation of the Speed Approach for UAV Collision Avoidance in Dynamic Environment,” 2018 Int. Multi-Conference Ind. Eng. Mod. Technol. FarEastCon 2018, pp. 1–6, 2018, doi: 10.1109/FarEastCon.2018.8602815.
[57]P. J. Burke, “Small Unmanned Aircraft Systems (SUAS) and Manned Traffic near John Wayne Airport (KSNA) Spot Check of the SUAS Facility Map: Towards a New Paradigm for Drone Safety Near Airports,” Drones, vol. 3, no. 4, pp. 1–11, 2019, doi: 10.3390/drones3040084.
[58]R. J. Wallace, K. W. Kiernan, T. Haritos, J. Robbins, and G. V. D’souza, “Evaluating small UAS near midair collision risk using AeroScope and ADS-B,” Int. J. Aviat. Aeronaut. Aerosp., vol. 5, no. 4, 2018, doi: 10.15394/ijaaa.2018.1268.
[59]J. H. Mott, Z. A. Marshall, M. A. Vandehey, M. May, and D. M. Bullock, “Detection of Conflicts Between ADS-B-Equipped Aircraft and Unmanned Aerial Systems,” Transp. Res. Rec., vol. 2674, no. 1, pp. 197–204, 2020, doi: 10.1177/0361198119900645.
[60]J. Jacob, T. Mitchell, J. Loffi, M. Vance, and R. Wallace, “Airborne visual detection of small unmanned aircraft systems with and without ADS-B,” 2018 IEEE/ION Position, Locat. Navig. Symp. PLANS 2018 - Proc., pp. 749–756, 2018, doi: 10.1109/PLANS.2018.8373450.
[61]I. A. Meer, M. Ozger, M. Lundmark, K. W. Sung, and C. Cavdar, “Ground Based Sense and Avoid System for Air Traffic Management,” IEEE Int. Symp. Pers. Indoor Mob. Radio Commun. PIMRC, vol. 2019-Septe, pp. 1–6, 2019, doi: 10.1109/PIMRC.2019.8904324.
[62]M. J. Arpaio, G. Paolini, F. Fuschini, A. Costanzo, and D. Masotti, “An all-in-one dual band blade antenna for ads-b and 5g communications in uav assisted wireless networks,” Sensors, vol. 21, no. 17, 2021, doi: 10.3390/s21175734.
[63]S. Siewert, K. Sampigethaya, J. Buchholz, and S. Rizor, “Fail-Safe, Fail-Secure Experiments for Small UAS and UAM Traffic in Urban Airspace,” AIAA/IEEE Digit. Avion. Syst. Conf. - Proc., vol. 2019-Septe, 2019, doi: 10.1109/DASC43569.2019.9081710.
[64]F. Svanström, C. Englund, and F. Alonso-Fernandez, “Real-time drone detection and tracking with visible, thermal and acoustic sensors,” Proc. - Int. Conf. Pattern Recognit., pp. 7265–7272, 2020, doi: 10.1109/ICPR48806.2021.9413241.
[65]J. Grzybowski, K. Latos, and R. Czyba, “Low-Cost Autonomous UAV-Based Solutions to Package Delivery Logistics,” in The 20th Polish Control Conference, 2020, pp. 500–507.
[66]E. Shaikh, N. Mohammad, and S. Muhammad, “Model Checking Based Unmanned Aerial Vehicle (UAV) Security Analysis,” in International Conference on Communications, Signal Processing, and their Applications (ICCSPA), 2021, no. April, pp. 1–6, doi: 10.1109/iccspa49915.2021.9385754.
[67]J. Wang, D. Song, R. Liang, and P. Han, “ADS-B and UAV signal monitoring system design based on spectrum sensing,” Proc. 2019 IEEE 8th Jt. Int. Inf. Technol. Artif. Intell. Conf. ITAIC 2019, no. Itaic, pp. 422–426, 2019, doi: 10.1109/ITAIC.2019.8785464.
[68]Y. Wu, H. N. Dai, H. Wang, and K. K. R. Choo, “Blockchain-Based Privacy Preservation for 5G-Enabled Drone Communications,” IEEE Netw., vol. 35, no. 1, pp. 50–56, 2021, doi: 10.1109/MNET.011.2000166.
[69]A. Bateman, J. Burkholder, T. Summers, and N. Richards, “Incorporating rf coverage analysis in mission planning for future airspace operations,” AIAA Scitech 2019 Forum, no. January, 2019, doi: 10.2514/6.2019-1059.
[70]S. Kunze and A. Weinberger, “Concept for a Geo-Awareness-System for Civilian Unmanned Aerial Systems,” 31st Int. Conf. Radioelektronika, 2021, doi: 10.1109/RADIOELEKTRONIKA52220.2021.9420196.
[71]X. Yue, Y. Liu, J. Wang, H. Song, and H. Cao, “Software Defined Radio and Wireless Acoustic Networking for Amateur Drone Surveillance,” IEEE Commun. Mag., vol. 56, no. 4, pp. 90–97, 2018, doi: 10.1109/MCOM.2018.1700423.
[72]C. E. Lin, P. Shao, and Y. Lin, “System Operation of Regional UTM in Taiwan,” Aerospace, 2020.
[73]H. Baek and J. Lim, “Design of Future UAV-Relay Tactical Data Link for Reliable UAV Control and Situational Awareness,” IEEE Commun. Mag., vol. 56, no. 10, pp. 144–150, 2018, doi: 10.1109/MCOM.2018.1700259.
[74]F. Minucci, E. Vinogradov, H. Sallouha, and S. Pollin, “UAV Location Broadcasting with Wi-Fi SSID,” IFIP Wirel. Days, no. April, 2019, doi: 10.1109/WD.2019.8734208.
[75]E. Vinogradov, F. Minucci, and S. Pollin, “Wireless communication for safe UAVs: From long-range deconfliction to short-range collision avoidance,” IEEE Veh. Technol. …, 2020, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9061133/.
[76]P. C. Shao, “Risk assessment for UAS logistic delivery under UAS traffic management environment,” Aerospace, vol. 7, no. 10, 2020, doi: 10.3390/AEROSPACE7100140.
[77]E. Perez et al., “Autonomous Collision Avoidance System for a Multicopter using Stereoscopic Vision,” 2018 Int. Conf. Unmanned Aircr. Syst. ICUAS 2018, vol. 91768, pp. 579–588, 2018, doi: 10.1109/ICUAS.2018.8453417.
[78]M. A. Abdel-Malek, K. Akkaya, A. Bhuyan, M. Cebe, and A. S. Ibrahim, “Enabling Second Factor Authentication for Drones in 5G using Network Slicing,” 2020 IEEE Globecom Work. GC Wkshps 2020 - Proc., pp. 1–6, 2020, doi: 10.1109/GCWkshps50303.2020.9367441.
[79]V. Kuroda, M. Egorov, S. Munn, and A. Evans, “Unlicensed Technology Assessment for Uas Communications,” Integr. Commun. Navig. Surveill. Conf. ICNS, vol. 2020-Septe, pp. 1–12, 2020, doi: 10.1109/ICNS50378.2020.9222903.
[80]P. Tedeschi, S. Sciancalepore, and R. Di Pietro, “ARID: Anonymous Remote IDentification of Unmanned Aerial Vehicles,” in Annual Computer Security Application Conference, 2021, pp. 207–218, doi: 10.1145/3485832.3485834.
[81]R. Alkadi and A. Shoufan, “Unmanned Aerial Vehicles Traffic Management Solution Using Crowd-sensing and Blockchain,” IEEE Trans. Netw. Serv. Manag., pp. 1–14, 2021, doi: 10.36227/techrxiv.16869997.v1.
[82]A. Alsoliman, A. Bin Rabiah, and M. Levorato, “Privacy-Preserving Authentication Framework for UAS Traffic Management Systems,” 2020 4th Cyber Secur. Netw. Conf. CSNet 2020, 2020, doi: 10.1109/CSNet50428.2020.9265534.
[83]A. Shoufan, C. Yeob Yeun, and B. Taha, “eSIM‐Based Authentication Protocol for UAV Remote Identification,” Secur. Priv. Internet Things, pp. 91–122, 2021, doi: 10.1002/9781119607755.ch4.
[84]J. Rios, A. Aweiss, J. Jung, J. Homola, M. Johnson, and R. Johnson, “Flight Demonstration of Unmanned Aircraft System ( UAS ) Traffic Management ( UTM ) at Technical Capability Level 4,” 2020, doi: 10.2514/6.2020-2851.
[85]G. Hunter, P. Wei, and C. Wargo, “Service-oriented separation assurance for small UAS traffic management,” Integr. Commun. Navig. Surveill. Conf. ICNS, vol. 2019-April, pp. 1–11, 2019, doi: 10.1109/ICNSURV.2019.8735193.
[86]M. Marques, A. Brum, S. Antunes, and J. G. Mota, “Sense and avoid implementation in a small unmanned aerial vehicle,” 13th APCA Int. Conf. Control Soft Comput. Control. 2018 - Proc., pp. 395–400, 2018, doi: 10.1109/CONTROLO.2018.8514548.
[87]C. Perner and C. Schmitt, “Security concept for unoccupied aerial systems,” AIAA/IEEE Digit. Avion. Syst. Conf. - Proc., vol. 2020-Octob, 2020, doi: 10.1109/DASC50938.2020.9256659.
[88]A. Shelley, “Drone Registration Will Not Prevent Another Gatwick,” SSRN Electron. J., no. December 2018, pp. 1–14, 2019, doi: 10.2139/ssrn.3378277.
[89]J. Athavale, A. Baldovin, S. Mo, and M. Paulitsch, “Chip-level considerations to enable dependability for eVTOL and urban air mobility systems,” in AIAA/IEEE Digital Avionics Systems Conference - Proceedings, 2020, pp. 1–6, doi: 10.1109/DASC50938.2020.9256436.
[90]A. Bauranov and J. Rakas, “Designing airspace for urban air mobility: A review of concepts and approaches,” Prog. Aerosp. Sci., vol. 125, p. 100726, 2021, doi: 10.1016/j.paerosci.2021.100726.
[91]Y. Zhi, Z. Fu, X. Sun, and J. Yu, “Security and Privacy Issues of UAV: A Survey,” Mob. Networks Appl., vol. 25, no. 1, pp. 95–101, 2019, doi: 10.1007/s11036-018-1193-x.
[92]R. Wuerll, J. Robert, and A. Heuberger, “An overview of current and proposed communication standards for large deployment of Unmanned Aircraft Systems,” IEEE Aerosp. Conf. Proc., vol. 2019-March, pp. 1–7, 2019, doi: 10.1109/AERO.2019.8742141.
[93]M. A. Benzaghta, A. Elwalda, M. Mousa, I. Erkan, and M. Rahman, “SWOT analysis applications: An integrative literature review,” J. Glob. Bus. Insights, vol. 6, no. 1, pp. 55–73, 2021, doi: 10.5038/2640-6489.6.1.1148.
[94]M. Khoshbakht, Z. Gou, and K. Dupre, “Cost-benefit Prediction of Green Buildings: SWOT Analysis of Research Methods and Recent Applications,” Procedia Eng., vol. 180, pp. 167–178, 2017, doi: 10.1016/j.proeng.2017.04.176.
[95]P. Institute, “Network Remote ID vs. Broadcast Remote ID,” 2022. https://pilotinstitute.com/broadcast-vs-network-remote-id/ (accessed Jul. 18, 2023).
[96]I. Gheorghisor, A. Chen, L. Globus, T. Luc, and P. Schrader, “RELIABLE 4G / 5G-BASED COMMUNICATIONS IN THE NATIONAL AIRSPACE : A UAS C2 USE CASE M & S Framework sUAS Information Flows,” in Integrated Communications Navigation and Surveillance (ICNS) Conference, 2020, pp. 1–14.
[97]Verizon, “What is 4G LTE and why it matters,” Verizon, 2018. https://www.verizon.com/about/news/what-4g-lte-and-why-it-matters (accessed Jul. 19, 2023).
[98]S. M. Kerner, “Definition of 4G (fourth-generation wireless),” TechTarget, 2021. https://www.techtarget.com/searchmobilecomputing/definition/4G (accessed Jul. 19, 2023).
[99]E. Ozturk, F. Erden, and I. Guvenc, “RF-Based Low-SNR Classification of UAVs Using Convolutional Neural Networks,” ArXiv Prepr., pp. 1–18, 2020, [Online]. Available: http://arxiv.org/abs/2009.05519.
[100]X. He, M. Wu, J. Miao, and C. Zhang, “The impact of channel environment on the RSRP and RSRQ measurement of handover performance,” 2011 Int. Conf. Electron. Commun. Control. ICECC 2011 - Proc., pp. 540–543, 2011, doi: 10.1109/ICECC.2011.6067737.
[101]J. L. Rios, I. S. Smith, P. Venkatesen, J. R. Homola, M. A. Johnson, and J. Jung, “UAS Service Supplier Specification: Baseline requirements for providing USS services within the UAS Traffic Management System,” 2019. [Online]. Available: http://www.sti.nasa.gov.
[102]A. Hately et al., “U-space Concept of Operations: Exploratory Research,” 2019.
[103]Federal Aviation Administration, “Airspace 101 – Rules of the Sky,” 2021. https://www.faa.gov/uas/getting_started/where_can_i_fly/airspace_101 (accessed Sep. 01, 2023).
[104]B. Hofmann-Wellenhof, H. Lichtenegger, and E. Wasle, GNSS Global Navigation Satellite Systems. Springer, 2008.
[105]V. Zaliva and F. Franchetti, “BAROMETRIC AND GPS ALTITUDE SENSOR FUSION,” 2014, doi: 10.1109/ICASSP.2014.6855063.
[106]C. Hajiyev, U. Hacizade, and D. Cilden-Guler, “Integration of barometric and GPS altimeters via adaptive data fusion algorithm,” Int. J. Adapt. Control Signal Process., vol. 35, no. 1, pp. 2–14, 2021, doi: 10.1002/acs.3184.
[107]G. Skorobogatov, C. Barrado, and E. Salamí, “Multiple UAV Systems: A Survey,” Unmanned Syst., vol. 8, no. 2, pp. 149–169, 2020, doi: 10.1142/S2301385020500090.
[108]N. Ruseno and C. Lin, “Development of UTM Monitoring System Based on Network Remote ID with Inverted Teardrop Detection Algorithm,” Unmanned Syst., 2023, doi: 10.1142/S2301385025500074.
[109]S. Jafer, S. Jones, and A. V. Raja, “A modeling and simulation framework for UAVs utilizing 4G-LTE cellular networks,” Int. J. Model. Simulation, Sci. Comput., vol. 9, no. 5, 2018, doi: 10.1142/S1793962318500423.
[110]E. Community, “Distance on a sphere: The Haversine Formula,” 2017. https://community.esri.com/t5/coordinate-reference-systems-blog/distance-on-a-sphere-the-haversine-formula/ba-p/902128 (accessed Jul. 16, 2022).
[111]O. Wiki, “Slippy map tilenames,” 2022. https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames#X_and_Y (accessed Jul. 16, 2022).
[112]J. J. Acevedo, C. Capitan, J. Capitiin, A. R. Castano, and A. Ollero, “A Geometrical Approach based on 4D Grids for Conflict Management of Multiple UAVs operating in U-space,” 2020 Int. Conf. Unmanned Aircr. Syst. ICUAS 2020, pp. 263–270, 2020, doi: 10.1109/ICUAS48674.2020.9213929.
[113]C. Carbone, U. Ciniglio, F. Corraro, and S. Luongo, “A novel 3D geometric algorithm for aircraft autonomous collision avoidance,” Proc. IEEE Conf. Decis. Control, pp. 1580–1585, 2006, doi: 10.1109/cdc.2006.376742.
[114]P. Zhao, H. Erzberger, and Y. Liu, “Multiple-Aircraft-Conflict Resolution Under Uncertainties,” J. Guid. Control. Dyn., vol. 44, no. 11, pp. 2031–2049, 2021, doi: 10.2514/1.G005825.
[115]N. Pongsakornsathien et al., “A Performance-based Airspace Model for Unmanned Aircraft Systems Traffic Management,” Aerospace, vol. 7, no. 11, pp. 1–25, 2020, doi: 10.3390/aerospace7110154.
[116]Wikipedia, “Field of view.” https://en.wikipedia.org/wiki/Field_of_view (accessed Jan. 20, 2023).
[117]F. Ho, R. Geraldes, A. Goncalves, M. Cavazza, and H. Prendinger, “Improved Conflict Detection and Resolution for Service UAVs in Shared Airspace,” IEEE Trans. Veh. Technol., vol. 68, no. 2, pp. 1231–1242, 2019, doi: 10.1109/TVT.2018.2889459.
[118]A. Weinert, L. Alvarez, M. Owen, and B. Zintak, “Near Midair Collision Analog for Drones Based on Unmitigated Collision Risk,” J. Air Transp., vol. 30, no. 2, pp. 37–48, 2022, doi: 10.2514/1.D0260.
[119]A. Weinert, S. Campbell, A. Vela, D. Schuldt, and J. Kurucar, “Well-clear recommendation for small unmanned aircraft systems based on unmitigated collision risk,” J. Air Transp., vol. 26, no. 3, pp. 113–122, 2018, doi: 10.2514/1.D0091.
[120]R. Raheb, S. James, A. Hudak, and A. Lacher, “Impact of Communications Quality of Service (QoS) on Remote ID as an Unmanned Aircraft (UA) Coordination Mechanism,” 2021.
[121]N. Ruseno, C. Lin, and W. Guan, “Flight Test Analysis of UTM Conflict Detection Based on a Network Remote ID Using a Random Forest Algorithm,” Drones, vol. 7, 2023, doi: https://doi.org/10.3390/drones7070436.
[122]C. Y. Tan, S. Huang, K. K. Tan, R. S. H. Teo, W. Q. Liu, and F. Lin, “Collision Avoidance Design on Unmanned Aerial Vehicle in 3D Space,” Unmanned Syst., vol. 6, no. 4, pp. 277–295, 2018, doi: 10.1142/S2301385018500115.
[123]DroneTag, “What is Remote ID?,” 2023. https://drone-remote-id.com/ (accessed Jul. 24, 2023).
[124]DroneTag, “All-in-One Solution for Safe Drone Flights,” 2020. https://dronetag.cz/en/product/ (accessed Mar. 07, 2022).
[125]M. F. Ahmad Fauzi, R. Nordin, N. F. Abdullah, and H. A. H. Alobaidy, “Mobile Network Coverage Prediction Based on Supervised Machine Learning Algorithms,” IEEE Access, vol. 10, pp. 55782–55793, 2022, doi: 10.1109/ACCESS.2022.3176619.
[126]H. Elsherbiny, H. M. Abbas, H. Abou-Zeid, H. S. Hassanein, and A. Noureldin, “4G LTE Network Throughput Modelling and Prediction,” 2020 IEEE Glob. Commun. Conf. GLOBECOM 2020 - Proc., 2020, doi: 10.1109/GLOBECOM42002.2020.9322410.
[127]H. K. Lee et al., “Critical parameter identification for safety events in commercial aviation using machine learning,” Aerospace, vol. 7, no. 6, pp. 1–24, 2020, doi: 10.3390/AEROSPACE7060073.
[128]H. S. Jo, C. Park, E. Lee, H. K. Choi, and J. Park, “Path loss prediction based on machine learning techniques: Principal component analysis, artificial neural network and gaussian process,” Sensors (Switzerland), vol. 20, no. 7, 2020, doi: 10.3390/s20071927.
[129]J. Q. Shi and T. Choi, Gaussian Process Regression Analysis for Functional Data. CRC Press, 2011.
[130]S. Duangsuwan and M. M. Maw, “Comparison of path loss prediction models for UAV and IoT air-to-ground communication system in rural precision farming environment,” J. Commun., vol. 16, no. 2, pp. 60–66, 2021, doi: 10.12720/jcm.16.2.60-66.
[131]O. Mbaabu, “Introduction to Random Forest in Machine Learning,” Section. https://www.section.io/engineering-education/introduction-to-random-forest-in-machine-learning/#:~:text=A random forest is a machine learning technique that’s used,consists of many decision trees. (accessed Jun. 20, 2023).
[132]Madison Schott, “Random Forest Algorithm for Machine Learning,” Capital One Tech, 2019. https://medium.com/capital-one-tech/random-forest-algorithm-for-machine-learning-c4b2c8cc9feb (accessed Jan. 24, 2023).
[133]D. G. da Silva, M. T. B. Geller, M. S. dos S. Moura, and A. A. de M. Meneses, “Performance evaluation of LSTM neural networks for consumption prediction,” e-Prime - Adv. Electr. Eng. Electron. Energy, vol. 2, no. February, 2022, doi: 10.1016/j.prime.2022.100030.
[134]T. N. Wei, “Explaining negative R-squared,” Toward Data Science, 2022. https://towardsdatascience.com/explaining-negative-r-squared-17894ca26321 (accessed Feb. 22, 2023).
[135]P. J. Burke, “4G Signal Propagation at Ground Level,” IEEE Trans. Antennas Propag., vol. 70, no. 4, pp. 2891–2903, 2022, doi: 10.1109/TAP.2021.3137221.
[136]M. A. Zulkifley, M. Behjati, R. Nordin, and M. S. Zakaria, “Mobile network performance and technical feasibility of lte-powered unmanned aerial vehicle,” Sensors, vol. 21, no. 8, 2021, doi: 10.3390/s21082848.
[137]M. Gharib, S. Nandadapu, and F. Afghah, “An Exhaustive Study of Using Commercial LTE Network for UAV Communication in Rural Areas,” 2021 IEEE Int. Conf. Commun. Work. ICC Work. 2021 - Proc., 2021, doi: 10.1109/ICCWorkshops50388.2021.9473547.
[138]A. Scaloni, P. Cirella, M. Sgheiz, R. Diamanti, and D. Micheli, “Multipath and Doppler Characterization of an Electromagnetic Environment by Massive MDT Measurements From 3G and 4G Mobile Terminals,” IEEE Access, vol. 7, pp. 13024–13034, 2019, doi: 10.1109/ACCESS.2019.2892864.
[139]A. K. Ishihara, J. Rios, and P. Venkatesan, “Remote id for rapid assessment of flight and vehicle information,” AIAA Scitech 2019 Forum, no. January, pp. 1–12, 2019, doi: 10.2514/6.2019-2077.
[140]M. Akselrod, N. Becker, M. Fidler, and R. Lübben, “4G LTE on the road - What impacts download speeds most?,” IEEE Veh. Technol. Conf., vol. 2017-Septe, pp. 1–6, 2018, doi: 10.1109/VTCFall.2017.8288296.
[141]N. L. Mohd Kamal, Z. Sahwee, N. Norhashim, N. Lott, S. Abdul Hamid, and W. Hashim, “Throughput Performance of 4G-based UAV in a Sub-Urban Environment in Malaysia,” 8th Annu. IEEE Int. Conf. Wirel. Sp. Extrem. Environ., pp. 49–53, 2020, doi: 10.1109/WiSEE44079.2020.9262610.
[142]M. Behjati, M. A. Zulkifley, H. A. H. Alobaidy, R. Nordin, and N. F. Abdullah, “Reliable Aerial Mobile Communications with RSRP & RSRQ Prediction Models for the Internet of Drones: A Machine Learning Approach,” Sensors, vol. 22, no. 15, 2022, doi: 10.3390/s22155522.