İnsansız Hava Araçlarında (İHA) GPS ve IMU Sistem Performansının İncelenmesi

Öz

Bu makale, İnsansız Hava Araçlarının (İHA) navigasyon ve kontrolünde Küresel Konumlama Sistemi (GPS) ve Ataletsel Ölçüm Birimi (IMU) teknolojilerinin kritik rolünü incelemektedir. GPS, hassas küresel konumlama sağlarken, IMU yüksek frekanslı hareket ve yönelim verileri sunar. Ancak, GPS sinyal kesintileri ve IMU kayması, özellikle dinamik görevlerde veya GPS erişiminin olmadığı ortamlarda zorluklar yaratmaktadır. Bu inceleme, navigasyon güvenilirliğini artırmak için Kalman filtresi ve makine öğrenimi gibi ileri seviye sensör füzyon teknikleri kullanılarak GPS ve düşük maliyetli IMU sistemlerinin entegrasyonunu ele almaktadır. Çalışmada ayrıca donanım gelişmeleri, uyarlanabilir algoritmalar ve sürü navigasyonu gibi gelecekteki yönelimler ele alınarak operasyonel zorlukların üstesinden gelinmesi ve İHA'ların çeşitli uygulamalardaki potansiyelinin açığa çıkarılması hedeflenmektedir.

Anahtar Kelimeler

• İnsansız Hava Araçları
• GPS
• IMU
• Sensör Füzyonu
• Konum Doğruluğu
• Sistem Performansı
• Navigasyon

Review of GPS and IMU System Performance in Unmanned Aerial Vehicles (UAVs)

Öz

This paper reviews the pivotal role of Global Positioning System (GPS) and Inertial Measurement Unit (IMU) technologies in the navigation and control of Unmanned Aerial Vehicles (UAVs). GPS offers precise global positioning, while IMU provides high-frequency motion and orientation data. However, GPS signal interruptions and IMU drift pose challenges, particularly in dynamic or GPS-denied environments. This review explores the integration of GPS and low cost IMU systems through advanced sensor fusion techniques, such as Kalman filtering and machine learning, to enhance navigation reliability. Future directions, including advancements in hardware, adaptive algorithms, and swarm navigation, are discussed to address operational challenges and unlock the potential of UAVs in diverse applications.

Anahtar Kelimeler

• UAVs
• GPS
• IMU
• Sensor Fusion
• Positioning Accuracy
• System Performance
• Navigation

Kaynakça

1. [1] N. Tuśnio and W. Wróblewski, "The efficiency of drones usage for safety and rescue operations in an open area: A case from Poland," Sustainability, 2022, 14, 327, doi: https://doi.org/10.3390/su14010327.
2. [2] E. Choi and S. Chang, “A consumer tracking estimator for vehicles in GPS-free environments,” IEEE Trans. Consum. Electron., vol. 63, no. 4, pp. 450–458, Jan. 2018, doi: https://doi.org/10.1109/TCE.2017.015064.
3. [3] S. H. Oh and D.-H. Hwang, "Low-cost and high performance ultra-tightly coupled GPS/INS integrated navigation method," Advances in Space Research., Dec. 2017, doi: https://doi.org/10.1016/j.asr.2017.06.007.
4. [4] P. A. C. Widagdo, H. -H. Lee and C. -H. Kuo, "Limb motion tracking with inertial measurement units," 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada, 2017, pp. 582-587, doi: https://doi.org/10.1109/SMC.2017.8122669.
5. [5] F. Caron, E. Duflos, D. Pomorski, and P. Vanheeghe, "GPS/IMU data fusion using multisensor Kalman filtering: Introduction of contextual aspects," Information Fusion, vol. 7, no. 2, pp. 221-230, Jun. 2006, doi: https://doi.org/10.1016/j.inffus.2004.07.002.
6. [6] Z. Li, C. Su, Z. Su, H. Peng, Y. Wang, W. Chen, and Q. Wu, "Model Predictive Control Enabled UAV Trajectory Optimization and Secure Resource Allocation," Researchgate, Nov. 2024, doi: https://doi.org/10.48550/arXiv.2411.04423.
7. [7] M. S. Grewal, A. P. Andrews, C. G. Bartone “Global Navigation Satellite Systems, Inertial Navigation, and Integration”, John Wiley & Sons Inc, 2020.
8. [8] A. Leick, L. Rapoport, and D. Tatarnikov, “GPS Satellite Surveying”, 4th ed. Hoboken, NJ, USA: John Wiley & Sons Inc, 2015.

9. [9] A. Waegli and J. Skaloud, "Optimization of two GPS/MEMS-IMU integration strategies with application to sports," GPS Solutions, Springer-Verlag, vol. 13, pp. 315-326, Apr. 2009, doi: https://doi.org/10.1007/s10291-009-0124-5.
10. [10] P. D. Groves, “Principles of GNSS, inertial, and multi-sensor integrated navigation systems” 2nd ed-Artech House, 2013.
11. [11] B. Yun, K. Peng and B. M. Chen, "Enhancement of GPS Signals for Automatic Control of a UAV Helicopter System," IEEE International Conference on Control and Automation, Nov. 2007, pp. 1185-1189, doi: https://doi.org/10.1109/ICCA.2007.4376547.
12. [12] I. A. Faisal, T. W. Purboyo, and A. S. R. Ansori, "A Review of Accelerometer Sensor and Gyroscope Sensor in IMU Sensors on Motion Capture," Journal of Engineering and Applied Sciences, vol. 15, no. 3, pp. 826–829, Nov. 2020, doi: https://doi.org/10.36478/jeasci.2020.826.829.
13. [13] E. Shafiee, M. R. Mosavi, and M. Moazedi, "A Modified Imperialist Competitive Algorithm for Spoofing Attack Detection in Single-Frequency GPS Receivers,", Wireless Personal Communications, vol. 119, pp. 919-940, Feb. 2021, doi: https://doi.org/10.1007/s11277-021-08244-2.
14. [14] S. S. Naik and A. M. Achar, "Unmanned Aerial Vehicle Communication for Autonomous Operations," ResearchGate, Mar. 2024, doi: https://doi.org/10.13140/RG.2.2.14050.13769.
15. [15] G.V. Hristov, P. Z. Zahariev, and I. H. Beloev, "A review of the characteristics of modern unmanned aerial vehicles," Acta Technologica Agriculturae, vol. 19, no. 2, pp. 33-38, Jun. 2016, doi: https://doi.org/10.1515/ata-2016-0008.
16. [16] G. D. Aydin and S. Ozer, "Infrared Detection Technologies in Smart Agriculture: A Review,” International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2023 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), Istanbul, Turkiye, 2023, pp. 1-8, doi: https://doi.org/10.1109/ACEMP-OPTIM57845.2023.10287033.
17. [17] D. Titterton and J. Weston, “Strapdown Inertial Navigation Technology,” 2nd ed., The Institution of Electrical Engineers, 2004.
18. [18] T. L. Grigorie and R. M. Botez, "Miniaturized Inertial Sensors’ Noise Reduction by Using Redundant Linear Configurations," The 19th IASTED International Conference on Applied Simulation and ModelingAt: Crete, Greece, pp. 74-81, Jun. 2011, doi: https://doi.org/10.2316/P.2011.715-051.
19. [19] M. S. Amin, M. B. I. Reaz, S. S. Nasir, and M. A. S. Bhuiyan, "Low Cost GPS/IMU Integrated Accident Detection and Location System," Indian Journal of Science and Technology, vol. 9, no. 10, pp. 1-9, Mar. 2016, doi: https://doi.org/10.17485/ijst/2016/v9i10/80221.
20. [20] G. D. Aydin, "A Wafer Level Vacuum Packaging Technology For MEMS-Based Long-Wave İnfrared Sensors”, Aug. 2022.
21. [21] R. C. Avram, X. Zhang, J. Campbell, and J. Muse, "IMU Sensor Fault Diagnosis And Estimation For UAVs", International Federation of Automatic Control, vol. 48, no. 21, pp. 380–385, 2015, doi: https://doi.org/10.1016/j.ifacol.2015.09.556.
22. [22] L. L. Arnold and P. A. Zandbergen, "Positional accuracy of the Wide Area Augmentation System in consumer-grade GPS units," Computers & Geosciences, vol. 37, no. 7, pp. 883-892, Jul. 2011, doi: https://doi.org/10.1016/j.cageo.2010.12.011.
23. [23] A. Sathyan, S. Kukreti, S. Sridhar, and E. Kivelevitch, "Location Determination of an Unmanned Aerial Vehicle in a GPS-Denied, Hazard-Cluttered Indoor Environment," AIAA Infotech @ Aerospace Conference, Jan. 2015, doi: https://doi.org/10.2514/6.2015-2028.
24. [24] Y. Zhao, "GPS/IMU integrated system for land vehicle navigation based on MEMS," Royal Institute of Technology (KTH), Sep. 2011.
25. [25] Y. Wang, J. Mangnus, D. Kostić, H. Nijmeijer, and S. T. H. Jansen, "Vehicle state estimation using GPS/IMU integration,", SENSORS, 2011 IEEE, Limerick, Ireland, 2011, pp. 1815-1818, doi: https://doi.org/10.1109/ICSENS.2011.6127142.
26. [26] T. S. Bruggemann, D. G. Greer, and R. A. Walker, "GPS fault detection with IMU and aircraft dynamics," IEEE Transactions on Aerospace and Electronic Systems, vol. 47, no. 1, pp. 305-316, Jan. 2011, doi: https://doi.org/10.1109/TAES.2011.5705677.
27. [27] E. D. Kaplan and C. J. Hegarty, “Understanding GPS/GNSS: Principles and Applications,” 3rd ed., Artech House, 2017.
28. [28] M. Mostafa, J. Hutton, B. Raid, and R. Hill "GPS/IMU products - the Applanix approach,", Dec. 2003.
29. [29] F. Caron., E. Duflos, D. Pomorski, P. Vanheeghe, “GPS/IMU data fusion using multisensor Kalman filtering: Introduction of contextual aspects,” Information Fusion, vol. 7, no. 2, pp. 221-230, Jun. 2006, doi: https://doi.org/10.1016/j.inffus.2004.07.002.
30. [30] J. Meyer-Hilberg, T. Jacob, “High accuracy navigation and landing system using GPS/IMU system integration,” Proceedings of 1994 IEEE Position, Location and Navigation Symposium - PLANS'94, Las Vegas, NV, USA, 1994, pp. 298-305, doi: https://doi.org/10.1109/PLANS.1994.303327.
31. [31] Y. Kim, H. Bang, “Introduction to Kalman Filter and Its Applications”, Researchgate, Nov. 2018, doi: https://doi.org/10.5772/intechopen.80600.
32. [32] K. Gamagedara, T. Lee and M. Snyder, "Quadrotor State Estimation With IMU and Delayed Real-Time Kinematic GPS," in IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no. 5, pp. 2661-2673, Oct. 2021, doi: https://doi.org/10.1109/TAES.2021.3061795.
33. [33] A. Fakharian, T. Gustafsson, and M. Mehrfam, "Adaptive Kalman Filtering Based Navigation: An IMU/GPS Integration Approach," IEEE International Conference on Networking, Sensing and Control, 2011, pp. 181-185, doi: https://doi.org/10.1109/ICNSC.2011.5874871.
34. [34] A. Carron, M. Todescato, R. Carli, L. Schenato, and G. Pillonetto, "Machine Learning Meets Kalman Filtering," IEEE 55th Conference on Decision and Control (CDC), 2016, pp. 4594-4599, doi: https://doi.org/10.1109/CDC.2016.7798968.
35. [35] S. Mahfouz, F. Mourad-Chehade, P. Honeine, J. Farah, and H. Snoussi, "Target Tracking Using Machine Learning and Kalman Filter in Wireless Sensor Networks," IEEE Sensors Journal, vol. 14, no. 10, pp. 3715-3725, Oct. 2014, doi: https://doi.org/10.1109/JSEN.2014.2332098.
36. [36] C. Chen and X. Pan, "Deep Learning for Inertial Positioning: A Survey," IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 9, pp. 10506-10523, Sept. 2024, doi: https://doi.org/10.1109/TITS.2024.3381161.
37. [37] P. Narkhede, R. Walambe, S. Poddar, K. Kotecha. 2021.” Incremental learning of LSTM framework for sensor fusion in attitude estimation,” PeerJ Computer Science, doi: https://doi.org/10.7717/peerj-cs.662.
38. [38] Y. Wang, J. Li, P. Zou, Z. Guo, and W. Li, "Research on UAV Path Planning based on LSTM," 2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS), Kalaburagi, India, 2023, pp. 1-6, doi: https://doi.org/10.1109/ICIICS59993.2023.10421651.
39. [39] A. Elamin, A. El-Rabbany, UAV-Based Multi-Sensor Data Fusion for Urban Land Cover Mapping Using a Deep Convolutional Neural Network. Remote Sens., 2022, 14, 4298., doi: https://doi.org/10.3390/rs14174298.
40. [40] Q. Feng, J. Yang, Y. Liu, C. Ou, D. Zhu, B. Niu, J. Liu, and B. Li, "Multi-Temporal Unmanned Aerial Vehicle Remote Sensing for Vegetable Mapping Using an Attention-Based Recurrent Convolutional Neural Network," Remote Sens., vol. 12, no. 10, pp. 1668, 2020, doi: https://doi.org/10.3390/rs12101668.
41. [41] Y. Chang, Y. Cheng, U. Manzoor, and J. Murray, "A review of UAV autonomous navigation in GPS-denied environments," Robotics and Autonomous Systems, vol. 170, Dec. 2023, Art. no. 104533, doi: https://doi.org/10.1016/j.robot.2023.104533.
42. [42] S. Fei, M. A. Hassan, Y. Xiao, X. Su, Z. Chen, Q. Cheng, F. Duan, R. Chen, and Y. Ma, "UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat," Precision Agriculture, 2022, vol. 24, pp. 187-212, doi: https://doi.org/10.1007/s11119-022-09938-8.