基于脉冲序列重构的雷达PRI调制类型智能识别算法研究
Research on Intelligent Recognition Algorithm for Radar PRI Modulation Type Based on Pulse Sequence Reconstruction
黄中瑞 1唐波1
作者信息
- 1. 国防科技大学电子对抗学院,安徽合肥 230037
- 折叠
摘要
雷达脉冲重复间隔(PRI)是雷达信号的关键参数,对于雷达性能发挥起着至关重要的作用.研判雷达PRI调制类型能够为雷达识别和性能分析提供重要支撑,是电子对抗领域的关键研究内容.针对现有PRI调制类型识别准确率受脉冲丢失影响较大的问题,本文提出了一种基于脉冲序列重构的雷达PRI调制类型智能识别算法.首先,基于到达时间(TOA)多阶差分序列实现了PRI调制周期的初步估计;其次,根据PRI调制周期重构了不同脉冲序列样本;再次,对重构样本进行去均匀值预处理,并以此为输入搭建了卷积神经网络(CNN)识别PRI调制类型,该方法能够减弱人工提取特征的局限性,不仅可以获取PRI序列的深层特征,而且具有更强的环境适用性;最后,仿真实验表明了所提方法的有效性.在脉冲丢失率小于60%时,所提方法对于所有PRI调制类型的识别准确率均在92%以上,平均识别准确率大于98%,在脉冲丢失率小于80%时,平均识别准确率在81%以上.
Abstract
The pulse repetition interval(PRI)is a key parameter of a radar signal and plays an important role in radar performance.The research on radar PRI modulation types can provide important support for radar recognition and perfor-mance analysis,and is a key research subject in the field of electronic countermeasures.An intelligent recognition algo-rithm based on pulse-sequence reconstruction is proposed to improve the accuracy of PRI modulation-type recognition,which is greatly affected by missing pulses.First,an initial estimation is made of the PRI modulation period based on the multi-order difference sequence of the time of arrival data.Second,samples of different pulse sequences are recon-structed based on the PRI modulation period.Furthermore,the pre-processing of the reconstructed samples is achieved by deducting the mean value.A convolutional neural network is then constructed to identify the PRI modulation type.The proposed method can reduce the limitation of manual feature extraction.In addition to determining the deep features of a PRI sequence,it has greater environmental applicability.Finally,simulation results are presented to verify the effi-ciency of the proposed method.When the pulse loss rate is less than 60%,the recognition accuracy of the proposed method for all PRI modulation types is greater than 92%,and the average recognition accuracy is greater than 98%.When the pulse loss rate is less than 80%,the average recognition accuracy is above 81%.
关键词
脉冲丢失/PRI调制周期/卷积神经网络/PRI调制类型识别Key words
pulse missing/PRI modulation period/convolutional neural network/PRI modulation type recognition引用本文复制引用
基金项目
国防科技大学学校科研计划(JS20-08)
出版年
2024
国家科技期刊平台
NSTL
万方数据