Preferences help
enabled [disable] Abstract
Number of results
2010 | 118 | 1 | 164-167
Article title

Structure Optimization of Phase-Coded Sounding Signals

Title variants
Languages of publication
Pulse compression with a small sidelobe level is one of challenges in designing of modern radar, sonar or ultrasound imaging systems. It may be performed by means of matched filter, that is by correlating the received sounding signal with a stored replica of the transmitted signal. The advantage of the pulse compression method is the increase of the average transmission power while retaining the range resolution corresponding to a short pulse. Phase coding is one of the early techniques for pulse compression of radar signals. Polyphase sequences, which have low sidelobe levels, ensure an easily detectable peak in the output of a matched filter, in other words an easy detection of a received sounding signal. In this paper, an evolutionary algorithm combined with a local optimizer is used to search for polyphase codes with a small sidelobe level of an aperiodic autocorrelation function. The evolutionary algorithm is based on a floating-point representation and the Gaussian mutation is used to produce offspring for the next generation. The self-adaptation mechanism is used to control the mutation operator during the evolutionary process. This research demonstrates that optimization methods can effectively find polyphase sequences with low autocorrelation and seems to be very promising for the future research in area of computer optimization for polyphase codes synthesis.
  • Institute of Telecommunications, University of Technology and Life Sciences, ul. Prof. S. Kaliskiego 7, 85-796 Bydgoszcz, Poland
  • Tele & Radio Research Institute, Ratuszowa 11, 03-450 Warszawa, Poland
  • 1. N. Levanon, E. Mozeson, in: Radar Signals, John Wiley & Sons, 2004, p. 100
  • 2. N. Mladenović, J. Petrović, V. Kovacević-Vujcić, M. Cangalović, European Journal of Operations Research 151, 389 (2003)
  • 3. T. Misaridis, Ultrasound imaging using coded signals, Technical university of Denmark, 2001, p. 61
  • 4. T. Felhauer, IEEE Trans. Aerosp. Electron. Syst., Vol. 30, 3, 869 (1994)
  • 5. Z. Michalewicz, D.B. Fogel, in: How to Solve It: Modern Heuristics, Springer Verlag, 2000, p. 276
  • 6. W. Findeisen, J. Szymanowski, A. Wierzbicki, in: Theory and Computational Methods of Optimization, PWN, Warszawa 1977, p. 186, (in Polish)
  • 7. M. Friese, H. Zottmann, Electron. Lett. 30, 1996
  • 8. P. Borwein, R. Ferguson, Information Theory, IEEE Transactions on Volume 51, 1564 2005
Document Type
Publication order reference
YADDA identifier
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.