Name
Abstract | ||
|---|---|---|
Chaotic sequences have been widely used as pseudorandom sequences. But the problem of how to judge their autocorrelation function's performances are good or not, up to now, have not been solved. In the paper, by method of phase space, we studied the autocorrelation function of chaotic sequence and discovered that their performance is determined by whether its phase space trajectory is axis symmetrical. This paper deduced theorems to describe and solve these problems, and presented a simple and effective method to judge autocorrelation performance of chaotic sequences, and a method to improve their autocorrelation performance was presented, too. Many simulations were presented to verify the theorems and methods. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/WCINS.2010.5541875 | WCNIS |
Keywords | Field | DocType |
autocorrelation,chaos,phase space,pseudorandom sequences,engineering management,radar,autocorrelation function,artificial neural networks,trajectory,correlation,signal processing,financial management,finance | Discrete mathematics,Applied mathematics,Maximum length sequence,Autocorrelation technique,Computer science,Autocorrelation matrix,Pseudorandom binary sequence,Complementary sequences,Chaotic,Autocorrelation,Distributed computing,Phase space method | Conference |
ISBN | Citations | PageRank |
978-1-4244-5850-9 | 2 | 0.46 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Chen | 1 | 4 | 0.91 |
| Peng Cai | 2 | 3 | 0.89 |
| Yong Zhang | 3 | 2 | 0.46 |
| Jian Huang | 4 | 2608 | 200.50 |
| Yunsong Wu | 5 | 6 | 3.07 |
| Jun Tang | 6 | 2 | 0.46 |