RadAlt Arızaları için EKF Sensör Füzyonlu Dijital İkiz Mimarisi
Yıl 2025,
Sayı: ERKEN GÖRÜNÜM, 1 - 1
Mustafa Enes Akçay
Seyfettin Vadi
Öz
Yapılan bu çalışma, bir altimetre sisteminin dijital ikizini oluşturarak olası radar altimetre (RadAlt) arızalarında gerçek zamanlı yedek bir irtifa sistemi sağlamayı hedeflemektedir. RadAlt sistemleri zaman zaman elektromanyetik girişimler, teknik arızalar ve çalışma aralığı gibi sebeplerden dolayı başarısız olabilmektedir. Dijital ikiz mimarisi tasarlanırken, genişletilmiş kalman filtre (EKF) tabanlı sensör füzyon tasarımı ile fiziksel bir sistemin sanal bir simülasyonu oluşturulmuştur. Tanımlanan arıza senaryoları doğrultusunda histerezis bir karar mekanizmasına sahip olan dijital ikiz mimarisi, tahmin algoritması arka planda sürekli çalışmasın rağmen sadece RadAlt arıza senaryosu gerçekleştiğinde sistemin dijital ikiz çıktısı vermesini sağlayarak sistemin kararlılığını sağlamaktadır. Gerçekçi sensör modelleri ve arıza koşulları ile bir uçuş profili tasarlanarak farklı test senaryolarında simülasyon test edilmiştir. Simülasyon sonuçları, RadAlt verisinin arıza senaryosunda dijital ikiz sisteminin güvenilir irtifa tahmini sağlayan sanal bir altimetre görevi gördüğü ve doğru irtifa bilgisi verdiği gözlemlenmiştir.
Kaynakça
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[1] M. Grieves and J. Vickers, "Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems," Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, pp. 85–113, 2017.
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[2] M. Grieves, Digital Twin: Manufacturing Excellence through Virtual Factory Replication, White Paper, vol. 1, pp. 1–7, 2014.
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[3] M. Holler, F. Uebernickel, and W. Brenner, "Digital twin concepts in manufacturing industries - a literature review and avenues for further research," in Proc. 18th Int. Conf. on Industrial Engineering (IJIE), Korean Institute of Industrial Engineers, Seoul, South Korea, 2016.
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[4] L. Weber, International Civil Aviation Organization (ICAO), 2023.
-
[5] B. A. Campbell, et al., "Calibration of Mars Reconnaissance Orbiter Shallow Radar (SHARAD) data for subsurface probing and surface reflectivity studies," Icarus, vol. 360, p. 114358, 2021.
-
[6] E. D. Kaplan and C. Hegarty, Understanding GPS/GNSS: Principles and Applications, Artech House, 2017.
-
[7] FAA, Radar Altimeter Minimum Operational Performance Standards, DO-228B, 2020.
-
[8] M. Skolnik, Radar Handbook, 3rd ed., McGraw-Hill, New York, 2008.
-
[9] S. Haykin, "Cognitive radar: A way of the future," IEEE Signal Processing Magazine, vol. 23, no. 1, pp. 30–40, 2006.
-
[10] F. Tao, et al., "Digital twin in industry: State-of-the-art," IEEE Transactions on Industrial Informatics, vol. 15, no. 4, pp. 2405–2415, 2018.
-
[11] L. Li, et al., "Digital twin in aerospace industry: A gentle introduction," IEEE Access, vol. 10, pp. 9543–9562, 2021.
-
[12] S. Boschert and R. Rosen, "Digital twin—The simulation aspect," in Mechatronic Futures: Challenges and Solutions for Mechatronic Systems and Their Designers, pp. 59–74, 2016.
-
[13] M. L. Fravolini, et al., "Experimental evaluation of two pitot-free analytical redundancy techniques for the estimation of the airspeed of an UAV," SAE Int. Journal of Aerospace, vol. 7, no. 2014-01-2163, pp. 109–116, 2014.
-
[14] O. Hazbon Alvarez, et al., "Digital twin concept for aircraft sensor failure," in Transdisciplinary Engineering for Complex Socio-Technical Systems, IOS Press, pp. 370–379, 2019.
[15] NASA, Digital Twin Technology in Space Systems for Mission Assurance, NASA Technical Brief, 2020.
-
[16] R. E. Kalman, "A new approach to linear filtering and prediction problems," Transactions of the ASME—Journal of Basic Engineering, 1960.
-
[17] M. S. Grewal, L. R. Weill, and A. P. Andrews, Global Positioning Systems, Inertial Navigation, and Integration, John Wiley & Sons, 2007.
-
[18] D. Hall and J. Llinas, Multisensor Data Fusion, CRC Press, 2001.
-
[19] A. M. Contreras and C. Hajiyev, "Robust Kalman filter-based fault-tolerant integrated baro-inertial-GPS altimeter," Metrology and Measurement Systems, pp. 673–686, 2019.
-
[20] R. K. Raney, Radar Altimeters, 2014.
-
[21] F. Balzano, et al., "Air data sensor fault detection with an augmented floating limiter," International Journal of Aerospace Engineering, vol. 2018, Article ID 1072056, 2018.
-
[22] K. Geng and N. Chulin, "Applications of multi-height sensors data fusion and fault-tolerant Kalman filter in integrated navigation system of UAV," Procedia Computer Science, vol. 103, pp. 231–238, 2017.
-
[23] ADS-B Exchange. (2024). Aircraft altitude data for regional jets.
Digital Twin Architecture with EKF Sensor Fusion for RadAlt Faults
Yıl 2025,
Sayı: ERKEN GÖRÜNÜM, 1 - 1
Mustafa Enes Akçay
Seyfettin Vadi
Öz
This study aims to develop a digital twin of an altimeter system to provide a real-time backup altitude solution in the event of possible Radar Altimeter (RadAlt) failures. RadAlt systems may occasionally fail due to reasons such as electromagnetic interference, technical malfunctions, or operational range limitations. During the design of the digital twin architecture, a virtual simulation of the physical system was created using an Extended Kalman Filter (EKF)-based sensor fusion scheme. In line with the defined fault scenarios, the digital twin architecture incorporates a hysteresis-based decision mechanism that ensures the prediction algorithm runs continuously in the background but only outputs the digital twin data when a RadAlt fault scenario occurs, thereby maintaining system stability. A flight profile was designed with realistic sensor models and fault conditions, and the system was tested under various simulation scenarios. Simulation results showed that, in the event of a RadAlt failure, the digital twin system functioned as a virtual altimeter providing reliable altitude estimation and accurate altitude information.
Kaynakça
-
[1] M. Grieves and J. Vickers, "Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems," Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, pp. 85–113, 2017.
-
[2] M. Grieves, Digital Twin: Manufacturing Excellence through Virtual Factory Replication, White Paper, vol. 1, pp. 1–7, 2014.
-
[3] M. Holler, F. Uebernickel, and W. Brenner, "Digital twin concepts in manufacturing industries - a literature review and avenues for further research," in Proc. 18th Int. Conf. on Industrial Engineering (IJIE), Korean Institute of Industrial Engineers, Seoul, South Korea, 2016.
-
[4] L. Weber, International Civil Aviation Organization (ICAO), 2023.
-
[5] B. A. Campbell, et al., "Calibration of Mars Reconnaissance Orbiter Shallow Radar (SHARAD) data for subsurface probing and surface reflectivity studies," Icarus, vol. 360, p. 114358, 2021.
-
[6] E. D. Kaplan and C. Hegarty, Understanding GPS/GNSS: Principles and Applications, Artech House, 2017.
-
[7] FAA, Radar Altimeter Minimum Operational Performance Standards, DO-228B, 2020.
-
[8] M. Skolnik, Radar Handbook, 3rd ed., McGraw-Hill, New York, 2008.
-
[9] S. Haykin, "Cognitive radar: A way of the future," IEEE Signal Processing Magazine, vol. 23, no. 1, pp. 30–40, 2006.
-
[10] F. Tao, et al., "Digital twin in industry: State-of-the-art," IEEE Transactions on Industrial Informatics, vol. 15, no. 4, pp. 2405–2415, 2018.
-
[11] L. Li, et al., "Digital twin in aerospace industry: A gentle introduction," IEEE Access, vol. 10, pp. 9543–9562, 2021.
-
[12] S. Boschert and R. Rosen, "Digital twin—The simulation aspect," in Mechatronic Futures: Challenges and Solutions for Mechatronic Systems and Their Designers, pp. 59–74, 2016.
-
[13] M. L. Fravolini, et al., "Experimental evaluation of two pitot-free analytical redundancy techniques for the estimation of the airspeed of an UAV," SAE Int. Journal of Aerospace, vol. 7, no. 2014-01-2163, pp. 109–116, 2014.
-
[14] O. Hazbon Alvarez, et al., "Digital twin concept for aircraft sensor failure," in Transdisciplinary Engineering for Complex Socio-Technical Systems, IOS Press, pp. 370–379, 2019.
[15] NASA, Digital Twin Technology in Space Systems for Mission Assurance, NASA Technical Brief, 2020.
-
[16] R. E. Kalman, "A new approach to linear filtering and prediction problems," Transactions of the ASME—Journal of Basic Engineering, 1960.
-
[17] M. S. Grewal, L. R. Weill, and A. P. Andrews, Global Positioning Systems, Inertial Navigation, and Integration, John Wiley & Sons, 2007.
-
[18] D. Hall and J. Llinas, Multisensor Data Fusion, CRC Press, 2001.
-
[19] A. M. Contreras and C. Hajiyev, "Robust Kalman filter-based fault-tolerant integrated baro-inertial-GPS altimeter," Metrology and Measurement Systems, pp. 673–686, 2019.
-
[20] R. K. Raney, Radar Altimeters, 2014.
-
[21] F. Balzano, et al., "Air data sensor fault detection with an augmented floating limiter," International Journal of Aerospace Engineering, vol. 2018, Article ID 1072056, 2018.
-
[22] K. Geng and N. Chulin, "Applications of multi-height sensors data fusion and fault-tolerant Kalman filter in integrated navigation system of UAV," Procedia Computer Science, vol. 103, pp. 231–238, 2017.
-
[23] ADS-B Exchange. (2024). Aircraft altitude data for regional jets.