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Polyphase orthogonal sequences design for opportunistic array radar via HGA

机译:通过HGA的机会阵列雷达的多相正交序列设计

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摘要

Opportunistic array radar(OAR) is a new generation radar system based on the stealth of the platform,which can improve the modern radar performance effectively.Designing the orthogonal code sets with low autocorrelation and cross-correlation is a key issue for OAR.This paper proposes a novel hybrid genetic algorithm(HGA) and designs the polyphase orthogonal code sets with low autocorrelation and cross-correlation properties,which can be used in the OAR system.The novel algorithm combines with simulated annealing(SA) and genetic algorithm(GA),adds in keeping best individuals and competition in small scope,and introduces grey correlation evaluation to evaluate fitness function. These avoid the premature convergence problem existed in GA and enhance the global searching capability.At last,the genetic results are optimized to obtain the best solution by using greedy algorithm.The simulation results show that the proposed algorithm is effective for the design of orthogonal phase signals used in OAR systems.

著录项

  • 来源
    《系统工程与电子技术(英文版)》 |2013年第1期|60-67|共8页
  • 作者单位

    College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210006 China;

    College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210006 China;

    Nanjing Research Institute of Electronics Technology Nanjing 210039 China;

    College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210006 China;

    College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210006 China;

    Nanjing Research Institute of Electronics Technology Nanjing 210039 China;

    College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210006 China;

  • 收录信息 中国科学引文数据库(CSCD);
  • 原文格式 PDF
  • 正文语种 chi
  • 中图分类
  • 关键词

  • 入库时间 2022-08-19 04:47:29
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