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A Differential Evolution Based Time-Frequency Atom Decomposition for Analyzing Emitter signals

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Modeling Decisions for Artificial Intelligence (MDAI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5861))

Abstract

This paper discusses the use of time-frequency atom decomposition based on a differential evolution to analyze radar emitter signals. Decomposing a signal into an appropriate time-frequency atoms is a well-known NP-hard problem. This paper applies a differential evolution to replace the traditional approach, a greedy strategy, to approximately solve this problem within a tolerable time. A large number of experiments conducted on various radar emitter signals verify the feasibilities that the time-frequency characteristics are shown by using a small number of decomposed time-frequency atoms, instead of traditional time-frequency distributions.

This work was supported by the National Natural Science Foundation of China (60702026) and the Scientific and Technological Funds for Young Scientists of Sichuan (09ZQ026-040).

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References

  1. Mallat, S.G., Zhang, Z.F.: Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing 41(12), 3397–3415 (1993)

    Article  MATH  Google Scholar 

  2. Ferreira da Silva, A.R.: Atomic decomposition with evolutionary pursuit. Digital Signal Processing 13, 317–337 (2003)

    Article  MathSciNet  Google Scholar 

  3. Gribonval, R., Bacry, E.: Harmonic decomposition of audio signals with matching pursuit. IEEE Transactions on Signal Processing 51(1), 101–111 (2003)

    Article  MathSciNet  Google Scholar 

  4. Lopez-Risueno, G., Grajal, J., Yeste-Ojeda, O.: Atomic decomposition-based radar complex signal interception. IEE Proceedings-Radar Sonar Navigation 150(4), 323–331 (2003)

    Article  Google Scholar 

  5. Tcheou, M.P., Lovisolo, L., da Silva, E.A.B., Rodrigues, M.A.M., Diniz, P.S.R.: Optimum rate-distortion dictionary selection for compression of atomic decompositions of electric disturbance signals. IEEE Transactions on Signal Processing Letters 14(2), 81–84 (2007)

    Article  Google Scholar 

  6. Davis, G., Mallat, S., Avellaneda, M.: Adaptive greedy approximation. Journal of Constructive Approximation 13(1), 57–98 (1997)

    MathSciNet  MATH  Google Scholar 

  7. Liu, Q.S., Wang, Q., Wu, L.N.: Size of the dictionary in matching pursuit algorithm. IEEE Transactions on Signal Processing 52(12), 3403–3408 (2004)

    Article  MathSciNet  Google Scholar 

  8. Vesin, J.: Efficient implementation of matching pursuit using a genetic algorithm in the continuous space. In: Proceedings of 10th European Signal Processing Conference, pp. 2–5 (2000)

    Google Scholar 

  9. Lopez-Risueno, G., Grajal, J.: Unknown signal detection via atomic decomposition. In: Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing, pp. 174–177 (2001)

    Google Scholar 

  10. Stefanoiu, D., Ionescu, F.: Faults diagnosis through genetic matching pursuit. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS, vol. 2773, pp. 733–740. Springer, Heidelberg (2003)

    Google Scholar 

  11. Zhang, G.X.: Time-frequency atom decomposition with quantum-inspired evolutionary algorithms. In: Circuits, Systems and Signal Process (accepted, 2009)

    Google Scholar 

  12. Storn, R., Price, K.: Differential evolution–a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012 (March 1995)

    Google Scholar 

  13. Wong, K.P., Dong, Z.Y.: Differential evolution, an alternative approach to evolutionary algorithm. In: Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, pp. 73–83 (2005)

    Google Scholar 

  14. Pant, M., Ali, M., Singh, V.P.: Differential evolution with parent centric crossover. In: Proceedings of the Second UKSIM European Symposium on Computer Modeling and Simulation, pp. 141–146 (2008)

    Google Scholar 

  15. Neri, N.F., Tirronen, V.: On memetic differential evolution frameworks: a study of advantages and limitations in hybridization. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2136–2142 (2008)

    Google Scholar 

  16. Qing, A.Y.: A study on base vector for differential evolution. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 550–556 (2008)

    Google Scholar 

  17. Cheng, J.X., Zhang, G.X.: Improved differential evolutions using a dynamic differential factor and population diversity. In: Proceedings of International Conference on Artificial Intelligence and Computational Intelligence (accepted, 2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031, Sichuan, P.R. China

    Gexiang Zhang

  2. School of Information Science & Technology, Southwest Jiaotong University, Chengdu, 610031, Sichuan, P.R. China

    Jixiang Cheng

Authors
  1. Gexiang Zhang
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  2. Jixiang Cheng
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Editor information

Editors and Affiliations

  1. IIIA-CSIC, Campus UAB s/n, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

  2. Toho Gakuen, 3-1-10 Naka, Kunitachi, 186-0004, Tokyo, Japan

    Yasuo Narukawa

  3. Graduate School of Engineering Science, Osaka University, 1-3, Machikaneyama, Toyonaka, 560-8531, Osaka, Japan

    Masahiro Inuiguchi

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© 2009 Springer-Verlag Berlin Heidelberg

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Zhang, G., Cheng, J. (2009). A Differential Evolution Based Time-Frequency Atom Decomposition for Analyzing Emitter signals. In: Torra, V., Narukawa, Y., Inuiguchi, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2009. Lecture Notes in Computer Science(), vol 5861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04820-3_15

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  • DOI: https://doi.org/10.1007/978-3-642-04820-3_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04819-7

  • Online ISBN: 978-3-642-04820-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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