北京航空航天大学学报 ›› 2018, Vol. 44 ›› Issue (6): 1124-1132.doi: 10.13700/j.bh.1001-5965.2017.0423

• 论文 • 上一篇    下一篇

基于FRFT域特征差异的压制干扰检测与分类算法

王国宏, 白杰, 张翔宇, 孙殿星   

  1. 海军航空大学 信息融合研究所, 烟台 264001
  • 收稿日期:2017-06-23 出版日期:2018-06-20 发布日期:2018-06-28
  • 通讯作者: 白杰.E-mail:1541753296@qq.com E-mail:1541753296@qq.com
  • 作者简介:王国宏 男,博士,教授,博士生导师。主要研究方向:抗干扰、信息融合、雷达组网;白杰 男,硕士,助理工程师。主要研究方向:信息融合、雷达抗干扰;张翔宇 男,博士,助理工程师。主要研究方向:机动目标跟踪、信息融合;孙殿星 男,博士,助理工程师。主要研究方向:机动目标跟踪、信息融合。
  • 基金资助:
    国家自然科学基金(61372027,61671462,61501489,61701519);泰山学者攀登计划

Detection and classification algorithm of suppression interference based on characteristic differences of FRFT domain

WANG Guohong, BAI Jie, ZHANG Xiangyu, SUN Dianxing   

  1. Institute of Information Fusion, Naval Aeronautical and Astronautical University, Yantai 264001, China
  • Received:2017-06-23 Online:2018-06-20 Published:2018-06-28

摘要: 针对干信比未知情况下有源压制干扰分类识别结果可信度较低的问题,提出了一种基于FRFT域特征差异的压制干扰检测与分类算法。首先,通过FRFT域峰值阶次的序贯判决算法,进行压制干扰的存在性检测,以保证压制干扰分类识别在较高的干信比条件下进行;然后,在此基础上,分别提取回波信号在FRFT域的极值阶次标准差和峰值阶次标准差作为分类识别特征量,同时,为避免硬判决造成的分类错误,采用模糊判决的方法得到基于不同特征参数的分类识别结果;最后,按一定准则将2种分类识别结果进行融合,以进一步提高分类识别正确率。仿真结果表明,与现有压制干扰分类识别算法相比,该算法较好地解决了分类识别结果可信度较低的问题,同时具有较高的分类识别正确率。

关键词: 有源压制干扰, 干扰检测, 分类识别, 模糊判决, 判决融合

Abstract: Aimed at the problem that the reliability of the active suppression interference classification recognition result is low when the jamming-signal-ratio is unknown, a detection and classification algorithm of suppression interference based on characteristic differences of FRFT domain was proposed. Firstly, the existence judgment of suppression interference was carried out by the sequential decision algorithm of FRFT domain peak order to ensure that the suppression interference classification was carried out under the condition of high jamming-signal-ratio. On this basis, the standard deviation of the extreme value orders and the peak orders in FRFT domain were taken as the classification features, and at the same time, in order to avoid the classification error caused by the hard decision, the fuzzy decision method was used to obtain the classification recognition results which are based on different characteristic parameters. Finally, the two kinds of classification recognition results were merged to improve the accuracy of classification recognition results according to certain criteria. The simulation results show that compared with the existing classification algorithm, this algorithm can solve the problem of low confidence in the classification results. At the same time, this algorithm has a high accuracy of classification.

Key words: active suppression interference, interference detection, classification recognition, fuzzy decision, decision fusion

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