Chaff Echo Detecting and Removing Method using Naive Bayesian Network

나이브 베이지안 네트워크를 이용한 채프에코 탐지 및 제거 방법

  • Lee, Hansoo (Department of Electrical and Computer Engineering, Pusan National University) ;
  • Yu, Jungwon (Department of Electrical and Computer Engineering, Pusan National University) ;
  • Park, Jichul (Department of Electrical and Computer Engineering, Pusan National University) ;
  • Kim, Sungshin (Department of Electrical and Computer Engineering, Pusan National University)
  • 이한수 (부산대학교 전자전기컴퓨터공학과) ;
  • 유정원 (부산대학교 전자전기컴퓨터공학과) ;
  • 박지철 (부산대학교 전자전기컴퓨터공학과) ;
  • 김성신 (부산대학교 전자전기컴퓨터공학과)
  • Received : 2013.04.12
  • Accepted : 2013.09.02
  • Published : 2013.10.01


Chaff is a kind of matter spreading atmosphere with the purpose of preventing aircraft from detecting by radar. The chaff is commonly composed of small aluminum pieces, metallized glass fiber, or other lightweight strips which consists of reflecting materials. The chaff usually appears on the radar images as narrow bands shape of highly reflective echoes. And the chaff echo has similar characteristics to precipitation echo, and it interrupts weather forecasting process and makes forecasting accuracy low. In this paper, the chaff echo recognizing and removing method is suggested using Bayesian network. After converting coordinates from spherical to Cartesian in UF (Universal Format) radar data file, the characteristics of echoes are extracted by spatial and temporal clustering. And using the data, as a result of spatial and temporal clustering, a classification process for analyzing is performed. Finally, the inference system using Bayesian network is applied. As a result of experiments with actual radar data in real chaff echo appearing case, it is confirmed that Bayesian network can distinguish between chaff echo and non-chaff echo.


Supported by : 한국연구재단


  1. H. Y. Han, B. H. Heo, S. H. Jung, G. W. Lee, C. H. You, and J. H. Lee, "Elimination of chaff echoes in reflectivity composite from an operational weather radar network using infrared satellite data," Atmosphere. Korean Meteorological Society (in Korean), vol. 21, no. 3, pp. 285-300, Sep. 2011.
  2. B. H. Heo, C. H. You, W. K. Kim, W. I. Son, J. Y. Koo, and H. Y. Han, "Chaff echo recognition and removal algorithm," Weather Radar Center Technical Notes WRC2010-01 (in Korean), 2010.
  3. J. S. Park, D. J. Ham, H. Y. Han, and I. K. Hwang, "Chaff echo recognition using clustering and fuzzy inference method," Weather Radar Center Technical Notes WRC2011-04 (in Korean), 2011.
  4. Y. H. Kim, H. S. Lee, and S. S. Kim, "3D radar objects tracking and reflectivity profiling," International Journal of Fuzzy Logic and Intelligent Systems, vol. 22, no. 4, pp. 263-269, Dec. 2012.
  5. Y. H. Kim, S. S. Kim, H. Y. Han, B. H. Heo, and C. H. You, "Real-time detection and filtering of chaff clutter from single-polarization doppler radar data," Journal of Atmospheric and Oceanic Technology, vol. 30, no. 5, pp. 873-895, May 2013.
  6. C. Y. Hong, J. H. Park, T. S. Yoon, and J. B. Park, "A study on the bayesian recurrent neural network for time series prediction," Journal of Control, Automation, and Systems Engineering (in Korean), vol. 10, no. 12, pp. 1295-1304, Dec. 2004.
  7. D. Heckerman, "Bayesian networks for data mining," Data Mining and Knowledge Discovery, vol. 1, no. 1, pp. 79-119, Mar. 1997.
  8. R. E. Neapolitan, Learning Bayesian Networks, Pearson Prentice Hall, New Jersey, 2004.
  9. A. McCallum and K. Nigam, "A comparison of event models for naive bayes text classification," AAAI-98 Workshop on Learning for Text Categorization, Madison, USA, vol. 752, pp. 41-48, Jul. 1998.
  10. R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, Machine learning: An artificial Intelligence Approach, vol. 2, Morgan Kaufmann, Portland, 1986.
  11. H. S. Lee, J. W. Yu, J. C. Park, and S. S. Kim, "Chaff echo recognizing and removing method using bayesian network," Proc. of 2013 28th ICROS Annual Conference (in Korean), pp. 121-122, 2013.
  12. H. W. Jung and J. H. Lee, "A directional feature extraction method of each region for the classification of fingerprint images with various shapes," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 18, no. 9, pp. 887-893, Sep. 2012.
  13. J. H. Choi, D. An, and J. H. Gang, "Survey on prognostics and comparison study on the model-based prognostics," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 17, no. 11, pp. 1095-1100, Nov. 2011.

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