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GENETIC ALGORITHMS IN SEARCH AND ANALYSIS OF LOW AUTOCORRELATION BINARY SEQUENCES

机译:低自相关二元序列的搜索与分析中的遗传算法

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The design of sequences that have low side-lobes and peaky autocorrelation functions are desirable in spread spectrum communication and efficient radar reception. The search of such sequences becomes extremely difficult when the length of the sequence is increased. In this paper Genetic Algorithm is used to search Low Autocorrelated Binary Sequences (LABS). Three objective functions (Golay merit factor, low sidelobe energy, low values of autocorrelation coefficients other than zero-lag) can be treated separately by Genetic Algorithm and an exhaustive search is done on the final population. The number of generations is controlled so that the population generated will have different sequences with desired objective functions. In this analysis it is found that only some objective functions, like minimum sidelobe energy, plays a prominent role in the overall performance. These findings provide useful information on the solution space which can enhance the search process.
机译:在扩频通信和高效雷达接收中,希望具有低侧瓣和峰值自相关函数的序列的设计。当序列的长度增加时,对这种序列的搜索变得非常困难。在本文中,遗传算法用于搜索低自相关二进制序列(实验室)。通过遗传算法单独处理三个客观函数(Golay Merit因子,低侧瓣能量,除零滞后的自相关系数的低值),并且在最终群体上完成详尽的搜索。控制几代人数,使得产生的人口将具有不同的序列,具有期望的目标函数。在该分析中,发现只有一些客观函数,如最小的Sidelobe能量,在整体性能中起着突出的作用。这些发现提供了有关能够增强搜索过程的解决方案空间的有用信息。

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