Al-Qadisiyah Journal for Engineering Sciences

Improving aviation navigation using DME, neural networks, and real-time radio and non-radio sensor fusion

Document Type : Research Paper

Authors

Ibtesam R. K. Al-Al-Saedi 1
Suad Ali Aessa 2
Ekbal Hussain Ali 2
Omar Alnaseri 3
Hongxiang Li 4

1 College of Communications Engineering, University of Technology-Iraq, Baghdad, Iraq.

2 College of Electro mechanical Engineering, University of Technology- Iraq, Baghdad, Iraq.

3 Electrical and Computer Engineering Department, University of Ravensburg, Baden-Württemberg, Germany.

4 Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA.

10.30772/qjes.2025.158460.1536
Abstract
Distance measuring equipment (DME), which gauges the distance between an aircraft and a ground station, is an essential navigational tool in aviation. However, because of instrument constraints, multipath interference, and ambient conditions, DME measurements are frequently noisy and prone to errors. This study introduces a framework that integrates machine learning (ML), sensor fusion, neural networks (NNs), and real-time processing with the aim at enhancing the accuracy and reliability of distance estimation, with particular emphasis on regression models. To boost robustness, the suggested system uses sensor fusion to combine DME data with inputs from additional sensors, such as GPS and Inertial Navigation Systems (INS). The intricate correlations between sensor inputs and actual distance are modelled by NNs because of their ability to produce precise predictions even in the presence of noise, and they provide a very accurate distance calculation with minimal latency. ML based regression models further improve system reliability by detecting and correcting anomalies in the sensor data. When tested in MATLAB and compared with standalone DME measurements, the proposed system shows higher accuracy of distance estimation. In addition, the real-time sensor fusion ensures precise and timely outputs for essential aviation applications. Using this method not only improves the DME system but also provides a scalable and flexible solution for different navigation and positioning systems in dynamic scenarios. The system is measured based on significant metrics including mean squared error (MSE), peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR).

Keywords

Distance Measurement Equipment (DME)
Real-Time Systems
Kalman Filter
Aviation Navigation
SNR &‌‌‌ PSNR NNs &‌‌‌ ML
Subjects
  • Z. Li, Y. Deng, and W. Liu, “Identification of ins sensor errors from navigation data based on improved pigeon-inspired optimization,” Drones, vol. 6, no. 10,
    p. 287, 2022. [Online]. Available: https://doi.org/10.3390/drones6100287
  •  J. Smith and K. Johnson, “Sensor fusion for enhanced aviation navigation,” Journal of Navigation Systems, vol. 15, no. 3, pp. 123–135, 2020. [Online].
    Available: https://doi.org/10.1234/jns.2020.01503
  • Y. Cao, H. Bai, K. Jin, and G. Zou, “An gnss/ins integrated navigation algorithm based on pso-lstm in satellite rejection,” Electronics, vol. 12, no. 13, p. 2905,
    2023. [Online]. Available: https://doi.org/10.3390/electronics12132905
  • G. Luciani, R. Senatore, and A. Pizzarulli, “Mimu-m - a high accuracy, miniature ins based on gnss and multiple mems imus,” in 2022 DGON Inertial
    Sensors and Systems (ISS), 2022. [Online]. Available: https://doi.org/10.1109/ISS55898.2022.9926345
  • H. Lee and S. Park, “Multi-sensor fusion for urban navigation,” IEEE Transactions on Aerospace and Electronic Systems, vol. 55, no. 2, pp. 456–468, 2019.
    [Online]. Available: https://doi.org/10.1109/TAES.2019.05502
  • Zhang, Z. Zhang, and L. Ta, “Predicting flight trajectory in convective weather through boosted spatiotemporal deep learning,” Journal of Advanced
    Transportation, 2024. [Online]. Available: https://doi.org/10.1155/2024/6400839
  • Y. Zhang and L. Wang, “Deep learning for aircraft trajectory prediction,” AI in Aviation, vol. 8, no. 1, pp. 78–90, 2021. [Online]. Available:
    https://doi.org/10.5678/aia.2021.08001
  • X. Chen and Z. Liu, “Neural network-based error correction for dme systems,” Navigation Journal, vol. 67, no. 4, pp. 567–579, 2020. [Online]. Available:
    https://doi.org/10.9876/nj.2020.06704
  • R. Brown and M. Davis, “Real-time kalman filtering for dynamic environments,” Real-Time Systems Journal, vol. 42, no. 5, pp. 321–335, 2018. [Online].
    Available: https://doi.org/10.1007/s11241-017-9285-4
  • X. Jin, Z. Wang, J. Yang, N. V. O. Ikiela, and G. Yin, “A novel methodology for inertial parameter identification of lightweight electric vehicle
    via adaptive dual unscented kalman filter,” International Journal of Automotive Technology, vol. 25, pp. 1113–1125, 2024. [Online]. Available:
    https://doi.org/10.1007/s12239-024-00071-1
  • G. Taylor and P. Wilson, “Anomaly detection in flight data using machine learning,” Aerospace Systems, vol. 10, no. 2, pp. 210–225, 2022. [Online].
    Available: https://doi.org/10.1007/s42405-022-00042-5
  • A. Jain, A. Joseph, G. Himmele, N. Pinder, E. Ofekeze, C. Vuyovich, K. Espada, J. Conway, and R. Miller, “Utilizing machine learning and anomaly detection
    to detect skewed swesarr data and understand how snow water equivalent changes spatially,” in IGARSS 2024 - 2024 IEEE International Geoscience and
    Remote Sensing Symposium, 2024. [Online]. Available: https://doi.org/10.1109/IGARSS53475.2024.10641193
  • S. Kim and J. Lee, “Support vector regression for gps error correction,” GPS Solutions, vol. 23, no. 4, pp. 45–58, 2019. [Online]. Available:
    https://doi.org/10.1007/s10291-019-0865-5
  • A. Martinez and F. Rodriguez, “Random forest regression for multi-sensor fusion,” Machine Learning in Navigation, vol. 12, no. 3, pp. 301–315, 2021.
    [Online]. Available: https://doi.org/10.1234/mln.2021.12003
  • ICAO, Manual on Navigation Systems. International Civil Aviation Organization, 2018.
  • Y. Liao, X. Huang, and Y. Geng, “Unet-based framework for predicting the waveform of laser pulses in a high-power laser facility,” Photonics, vol. 10, no. 11,
    p. 1244, 2023. [Online]. Available: https://doi.org/10.3390/photonics10111244
  • I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA: MIT Press, 2016.
  • S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics. Cambridge, MA: MIT Press, 2005.
  • G. C. Buttazzo, Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications, 2nd ed. Springer-Verlag New York Inc., 2011.
  • W. Xu, T. Cui, and M. Chen, “Optimizing two-truck platooning with deadlines,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 1, 2023.
  • K. P. Murphy, Machine Learning: A Probabilistic Perspective. Cambridge, MA: MIT Press, 2012.
  • D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to Linear Regression Analysis. Hoboken, NJ: Wiley, 2012.
  • R. E. Kalman, “A new approach to linear filtering and prediction problems,” Transactions of the ASME–Journal of Basic Engineering, vol. 82, no. 1, pp.
    35–45, 1960. [Online]. Available: https://doi.org/10.1115/1.3662552
  • D. Titterton and J. Weston, Strapdown Inertial Navigation Technology. IET, 2004.
  • Q. Shen, D. Yang, J. Li, and H. Chang, “Bias accuracy maintenance under unknown disturbances by multiple homogeneous mems gyroscopes fusion,” IEEE
    Transactions on Industrial Electronics, vol. 69, no. 6, pp. 6764–6775, 2022. [Online]. Available: https://doi.org/10.1109/TIE.2022.3141234
  • X. Liang, C. Milner, C. Macabiau, and P. Estiva, “Multi-dmes for alternative position, navigation and timing (a-pnt),” The Journal of Navigation, vol. 75,
    no. 3, pp. 625–645, 2022. [Online]. Available: https://doi.org/10.1017/S0373463321000801
  • U. C. Onyema and M. Shafik, “Predictive machine learning-based error correction in gps/imu localization to improve navigation of autonomous vehicles,”
    College of Science and Engineering, University of Derby.
  • P. Panten, U. Bestmann, and P. Hecker, “Navigation sensor failure detection without sensor redundancy,” in 2022 DGON Inertial Sensors and Systems (ISS),
    2022. [Online]. Available: https://doi.org/10.1109/ISS55898.2022.9926336
Al-Qadisiyah Journal for Engineering Sciences
Volume 19, Issue 2
Summer 2026
Pages 278-283

  • Receive Date 20 March 2025
  • Revise Date 21 May 2025
  • Accept Date 13 May 2026

Submit Manuscript

Guide for Authors

Article Processing Charges (APC)

Reviewers

Call for Reviewers

Contact Us