Multipath Mitigation for Pulses Using Supervised Learning: Application to Distance Measuring Equipment

  • Kim, Euiho (Department of Aeronautical and Mechanical Engineering, Cheongju University)
  • Received : 2016.10.11
  • Accepted : 2016.11.11
  • Published : 2016.12.15


This paper presents a method to suppress multipath induced by pulses using supervised learning. In modern electronics, pulses have been used for various purposes such as communication or distance measurements. Like other signals, pulses also suffer from multipath. When a pulse and a multipath are overlapped, the original pulse shape is distorted. The distorted pulse could result in communication failures or distance measurement errors. However, a large number of samples available from a pulse can be used to effectively reject multipath by using a supervised learning method. This paper introduces how a supervised learning method can be applied to Distance Measuring Equipment. Simulation results show that multipath induced distance measuring error can be suppressed by 10 ~ 45 percent depending on the allowed pulse shape variation allowed in a standard.


Supported by : Cheongju University


  1. Adeli, H. & Ghosh-Dastidar, S. 2010, Automated EEG-based diagnosis of neurological disorders: Inventing the future of neurology (Boca Raton, FL: CRC Press).
  2. Boyd, S. & Vandenberghe, L. 2004, Convex optimization (Cambridge, NY: Cambridge University press).
  3. Carneiro, G., Chan, A. B., Moreno, P. J., &Vasconcelos, N. 2007, Supervised learning of semantic classes for image annotation and retrieval, IEEE transactions on pattern analysis and machine intelligence, 29, 394-410.
  4. Chan, F., Choi, J., & Jee, G.-I. 2005, Time Estimation of Superimposed Coherent Multipath Signals Using the EM Algorithm for Global Positioning System, Journal of Global Positioning System, 4, 56-64.
  5. Chapelle, O., Scholkopf, B., & Zien, A. 2006, Semi-supervised Learning (Cambridge, MA: MIT Press).
  6. Cord, M., Cunningham, P. & Joshi, D. 2009, Machine Learning Techniques for Multimedia: Case Studies on Organization and Retrieval, Journal of Electronic Imaging, 18, 039901- 01-2.
  7. Erman, J., Mahanti, A., Arlitt, M., Cohen, I., & Williamson, C. 2007, Semi-supervised network traffic classification, In ACM SIGMETRICS Performance Evaluation Review, 35, 369-70.
  8. Ghosh-Dastidar, S. & Adeli, H. 2009, A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection, Neural Networks, 22, 1419-1431.
  9. Kayton, M. & Fried, W. R. 1997, Avionics navigation systems, 2nd ed. (New York: John Wiley & Sons).
  10. Kelly, R. J. & Cusick, D. R. 1986, Distance measuring equipment and its evolving role in aviation, Advances in electronics and electron physics, 68, 1-243.
  11. Kim, E. 2012, Investigation of APNT optimized DME/DME network using current state-of-the-art DMEs: Ground station network, accuracy, and capacity, In Position Location and Navigation Symposium (PLANS), 2012 IEEE/ION, 146-57. IEEE.
  12. Kim, E. 2013a, Alternative DME/N pulse shape for APNT, In 2013 IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC), 4D2-1-4D2-10, IEEE.
  13. Kim, E. 2013b, Enhancing DME/N multipath rejection with tightened pulse waveform variation, In 2013 IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC), 4D1-1-4D1-9, IEEE.
  14. Lo, S., Chen, Y. H., Segal, B., Peterson, B., Enge, P., et al. 2014, Containing a Difficult Target: Techniques for Mitigating DME Multipath to Alternative Position Navigation and Timing (APNT), In Proceedings of the International Technical Meeting of The Institute of Navigation, San Diego, CA, pp.413-423.
  15. Lo, S., Peterson, B., Akos, D., Narins, M., Loh, R., et al. 2011, Alternative Position Navigation & Timing (APNT) Based on Existing DME and UAT Ground Signals, In Proceedings of the Institute of Navigation GNSS Conference, Portland, OR.
  16. Pelgrum, W., Li, K., Smearcheck, M., & van Graas, F. 2012, eDME architecture development and flight-test evaluation, In 2012 IEEE/AIAA 31st Digital Avionics Systems Conference (DASC), 1-37, IEEE.
  17. Smaragdis, P. 2007, Convolutive speech bases and their application to supervised speech separation, IEEE Transactions on Audio, Speech, and Language Processing, 15, 1-12.