A new approach to sequence construction with good correlation by particle swarm optimization

In this paper, a novel computationally affordable method to generate long binary sequences featuring desiredproperties is presented, based on the use of a number of shorter non linear binary sub-sequences. The paper shows therelationship of the Auto- and Cross-Correlation (AC, CC) of the generated long binary sequences with the AC and CC ofconstituent sub-sequences. It is also shown that the starting bit position of sub-sequences has an important role on AC andCC of the generated sequences. To generate the optimal long binary sequence from correlation points of view, Particle SwarmOptimization (PSO) algorithm is employed. All the techniques stated in the literature to improve the PSO are implemented andit is clearly shown that the constriction factor and the variable population size turn out to have a great impact on minimizing thefitness function (RMS of AC) representing the target Correlation properties expected for the resulting long sequence. Possibleapplication scenarios for the long sequences generated by the proposed method are also discussed and evaluated.