Abstract
The main problem of time-frequency atom decomposition (TFAD) lies in an extremely high computational load. This paper pres- ents a fast implementation method based on quantum-inspired genetic algorithm (QGA). Instead of finding the optimal atom in greedy implementation algorithm, this method is to search a satisfactory atom in every iteration of TFAD. Making full use of QGA’s advantages such as good global search capability, rapid convergence and short computing time, the method reduces greatly the computational load of TFAD. Experiments conducted on radar emitter signals verify the effectiveness and practicality of the introduced method.
This work was supported by the Scientific and Technological Development Foundation of Southwest Jiaotong University (2006A09) and by the National Natural Science Foundation of China (60572143).
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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)
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)
Mallat, S.G., Zhang, Z.F.: Matching Pursuits with Time-Frequency Dictionaries. IEEE Transactions on Signal Processing 41, 3397–3415 (1993)
Qian, S., Chen, D.: Signal Representation Using Adaptive Normalized Gaussian Functions. Signal Processing 36, 1–11 (1994)
Gribonval, R., Bacry, E.: Harmonic Decomposition of Audio Signals with Matching Pursuit. IEEE Transactions on Signal Processing 51, 101–111 (2003)
Figueras i Ventura, R.M., Vandergheynst, P.: Matching Pursuit through Genetic Algorithms. LTS-EPFL Tech. Report, 1–14 (2001)
Yin, Z.K., Wang, J.Y., Pierre, V.: Signal Sparse Decomposition Based on GA and Atom Property. Journal of the China Railway Society 27, 58–61 (2005)
Stefanoiu, D., Llonescu, F.: A Genetic Matching Pursuit Algorithm. In: Proceedings of 7th International Symposium on Signal Processing and Its Applications, pp. 577–580 (2003)
Gribonval, R.: Fast Matching Pursuit with a Multiscale Dictionary of Gaussian Chirps. IEEE Transactions on Signal Processing 49, 994–1001 (2001)
Zhang, G.X., Jin, W.D., Li, N.: An Improved Quantum Genetic Algorithm and Its Application. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 449–452. Springer, Heidelberg (2003)
Zhang, G.X., Hu, L.Z., Jin, W.D.: Quantum Computing Based Machine Learning Method and Its Application in Radar Emitter Signal Recognition. In: Torra, V., Narukawa, Y. (eds.) MDAI 2004. LNCS (LNAI), vol. 3131, pp. 92–103. Springer, Heidelberg (2004)
Zhang, G.X., Rong, H.N., Jin, W.D.: An Improved Quantum-Inspired Genetic Algorithm and Its Application to Time-Frequency Atom Decomposition. Dynamics of Continuous, Discrete and Impulsive Systems, to appear (2007)
Zhang, G.X., Rong, H.N., Jin, W.D., Hu, L.Z.: Radar Emitter Signal Recognition Based on Resemblance Coefficient Features. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 665–670. Springer, Heidelberg (2004)
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Zhang, G., Rong, H. (2007). Quantum-Inspired Genetic Algorithm Based Time-Frequency Atom Decomposition. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2007. ICCS 2007. Lecture Notes in Computer Science, vol 4490. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72590-9_35
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