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).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mallat, S.G., Zhang, Z.F.: Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing 41(12), 3397–3415 (1993)
Ferreira da Silva, A.R.: Atomic decomposition with evolutionary pursuit. Digital Signal Processing 13, 317–337 (2003)
Gribonval, R., Bacry, E.: Harmonic decomposition of audio signals with matching pursuit. IEEE Transactions on Signal Processing 51(1), 101–111 (2003)
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)
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)
Davis, G., Mallat, S., Avellaneda, M.: Adaptive greedy approximation. Journal of Constructive Approximation 13(1), 57–98 (1997)
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)
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)
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)
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)
Zhang, G.X.: Time-frequency atom decomposition with quantum-inspired evolutionary algorithms. In: Circuits, Systems and Signal Process (accepted, 2009)
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)
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)
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)
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)
Qing, A.Y.: A study on base vector for differential evolution. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 550–556 (2008)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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)