北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (8): 1574-1581.doi: 10.13700/j.bh.1001-5965.2019.0507

• 论文 • 上一篇    下一篇

基于IFA-HFS的雷达波形域LPI性能评估方法

杨诚修1, 王谦喆1, 彭卫东1, 李寰宇1, 裴少婷2   

  1. 1. 空军工程大学 空管领航学院, 西安 710051;
    2. 空军工程大学 航空工程学院, 西安 710038
  • 收稿日期:2019-09-16 发布日期:2020-08-27
  • 通讯作者: 王谦喆 E-mail:afeu_wqz@163.com
  • 作者简介:杨诚修 男,硕士研究生。主要研究方向:电子综合化工程。
    王谦喆 男,硕士,副教授,硕士生导师。主要研究方向:电子综合化技术。
    彭卫东 男,博士,教授,研究生导师。主要研究方向:信号处理。
  • 基金资助:
    国家自然科学基金(61773197)

Radar LPI performance evaluation method for waveform domain based on IFA-HFS

YANG Chengxiu1, WANG Qianzhe1, PENG Weidong1, LI Huanyu1, PEI Shaoting2   

  1. 1. Air Traffic Control and Navigation College, Air Force Engineering University, Xi'an 710051, China;
    2. Aeronautics Engineering College, Air Force Engineering University, Xi'an 710038, China
  • Received:2019-09-16 Published:2020-08-27
  • Supported by:
    National Natural Science Foundation of China (61773197)

摘要: 针对雷达波形域低截获(LPI)性能评估的问题,提出一种应用改进萤火虫算法(IFA)求解指标权重的犹豫模糊集(HFS)评估方法。首先,介绍基于逼近理想解排序(TOPSIS)的犹豫模糊集理论,并从属性和方案2个角度构建指标权重的优化模型;其次,通过引入混沌理论,解决了萤火虫算法容易陷入局部最优的问题,给出用IFA求解指标权重的流程;再次,从雷达发射方角度,提取脉内、脉间5个波形域LPI性能评估指标;最后,得到利用IFA求解指标权重的犹豫模糊集评估方法。选取4种不同类型的雷达进行仿真对比,获得波形域LPI性能排序,验证了方法的快速性和有效性。

关键词: 雷达波形域, 低截获(LPI)性能, 犹豫模糊集(HFS), 指标权重优化, 改进萤火虫算法(IFA)

Abstract: In order to solve the problem of Low Probability of Interception (LPI) performance evaluation of radar waveform domain, a Hesitant Fuzzy Set (HFS) evaluation method based on Improved Firefly Algorithm (IFA) is proposed to obtain index weight. Firstly, we introduce the HFS theory based on Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and construct an optimization model of index weights from the perspectives of attribute and scheme. Secondly, we solve the problem that firefly algorithm is easy to fall into local optimum by introducing chaos theory, and give the process to get index weights by using IFA. Then, we extract five LPI performance evaluation indicators between inter-pulse and intra-pulse information from the radar transmitter. Finally, the HFS evaluation method based on IFA is obtained to get optimal index weights. Four different types of radar are selected to simulate and compare the waveform domain LPI performance, and the ranking results are obtained, which verify the rapidity and effectiveness of the proposed algorithm.

Key words: radar waveform domain, Low Probability of Interception (LPI) performance, Hesitant Fuzzy Set(HFS), index weight optimization, Improved Firefly Algorithm (IFA)

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