In the context of spectrum surveillance, a method to
recover the code of spread spectrum signal is presented, whereas the
receiver has no knowledge of the transmitter-s spreading sequence.
The approach is based on a genetic algorithm (GA), which is forced to
model the received signal. Genetic algorithms (GAs) are well known
for their robustness in solving complex optimization problems.
Experimental results show that the method provides a good
estimation, even when the signal power is below the noise power.
 D. Thomas Magill, Francis D. Natali, Gwyn P. Edwards, "Spread
Spectrum Technology for Commercial Applications," Proceeding of the
IEEE, vol. 82, pp. 572-584, April. 1994.
 Raymond. L. Picholtz, Doland L. Schilling, Laurence B. Milstein,
"Theory of Spread Spectrum Communications - A Tutorial," IEEE
Transactions on Communications, vol. COM-30, pp. 855-884, May.
 John G. Proakis, Digital communication, Third Edition, Mac Graw Hill
International Editions, 1995.
 Dilip V. Sarwate, Michael B. Pursley, "Crosscorrelation Properties of
Pseudo-random and Related Sequences," Proceeding of the IEEE, vol.
68, pp. 593-619, May. 1980.
 Michail K. Tsatsanis, Georgios B. Giannakis, "Blind Estimation of
Direct Sequence Spread Spectrum Signals in Multipath," IEEE
Transactions on Signal Processing, vol. 45, pp. 1241-1252, May. 1997.
 J.-M. Renders and S. P. Flasse, "Hybrid methods using genetic
algorithms for global optimization," IEEE Trans. Systems, Man,
CyberneticsÔÇöPart B: Cybernetics, vol. 26, pp. 243-258, April. 1996.
 J. H. Holland, Adaptation in Natural and Artificial Systems, Ann Arbor,
MI: Univ. Michigan Press, 1975.
 M. Mitchell, An Introduction to Genetic Algorithms. Cambridge, MA:
MIT Press, 1996.
 D. E. Goldberg, Genetic Algorithms in Search, Optimization, and
Machine Learning. Reading, MA: Addison-Wesley, 1989.r publication.