针对常规方法对导航雷达辐射源识别正确率低的问题,通过分析研究导航雷达的信号时频特点和无意调制特性,重点关注脉冲信号的瞬时频率,特别是脉冲上升沿和下降沿处,对瞬时频率的变化率取一系列采样点组成向量作为特征,使用支持向量机做分类,基于仿真数据的实验表明,以脉冲前后沿的频率变化为特征比以整个脉冲的频率变化为特征更能区分不同的雷达辐射源信号,不仅提高了识别正确率,而且降低了计算量,该方法在单载频信号、线性调频信号和二相编码信号中获得了令人满意的结果。使用实际采集数据验证,在信噪比为20 dB时,识别正确率在90%以上。
Aiming at the low accuracy of recognition of navigation radar emitter by conventional methods, the time-frequency characteristics of pulse signals and unintentional modulation on pulse of navigation radar were analyzed and studied, focusing on the instantaneous frequency of pulse signals, especially at the rising and falling edges of pulses. The change rate of instantaneous frequency was characterized by taking a series of sampling point composition vectors and using support vector machine for classification. Experiments based on simulation data showed that, Compared with the whole pulse, this method can distinguish different radar emitter signals better, which not only improves the recognition accuracy, but also reduces the calculation amount. The method obtains satisfactory results in single carrier frequency signal, linear frequency modulation signal and binary phase-coded signal. Using the actual data collection verification, when the SNR is 20dB, the recognition accuracy is more than 90%.
2024,46(10): 147-151 收稿日期:2023-08-25
DOI:10.3404/j.issn.1672-7649.2024.10.025
分类号:TN971
作者简介:蓝天亮(1999-),男,硕士研究生,研究方向为雷达信号处理
参考文献:
[1] 孙丽婷, 黄知涛, 王翔, 等. 辐射源指纹特征提取方法述评[J]. 雷达学报, 2020, 9(6): 1014-1031.
SUN Li-ting, HUANG Zhi-tao, WANG Xiang, et al. Overview of radio frequency fingerprint extraction in specific emitter identification[J]. Journal of Radars, 2020, 9(6): 1014-1031.
[2] 罗彬珅, 刘利民, 刘璟麒. 基于包络前沿特性的干扰源个体识别研究[J]. 电光与控制, 2019, 26(12): 17-21.
LUO Bin-shen, LIU Li-min, LIU Jing-qi. Individual identification of jamming sources based on envelope front characteristics[J]. Electronics Optics & Control, 2019, 26(12): 17-21.
[3] BERTONCINI C, RUDD K, NOUSAIN B, et al. Wavelet fingerprinting of Radio-Frequency IDentification (RFID)tags[J]. IEEE Transactions on Industrial Electronics, 2012, 59(12): 4843-4850.
[4] 蔡忠伟, 李建东. 基于双谱的通信辐射源个体识别[J]. 通信学报, 2007, 28(2): 75-79.
CAI Zhong-wei, LI Jian-dong. Study of transmitter individual identification based on bispectra[J]. Journals on Communications, 2007, 28(2): 75-79.
[5] 任东方, 张涛, 韩洁, 等. 基于 ITD 与纹理分析的特定辐射源识别方法[J]. 通信学报, 2017, 38(12): 160-168.
REN Dong-fang, ZHANG Tao, HAN Jie, et al. Specific emitter identification based on ITD and texture analysis[J]. Journals on Communications, 2017, 38(12): 160-168.
[6] 张忠民, 刘刚. 基于 VMD 和 ABC-SVM 的雷达辐射源个体识别[J]. 哈尔滨商业大学学报(自然科学版), 2020, 36(2): 176-189.
ZHANG Zhong-min, LIU Gang. Individual identification of radar emitter based on VMD and ABC-SVM[J]. Journal of Harbin University of Commerce (Natural Sciences Edition), 2020, 36(2): 176-189.
[7] 武佳玥. 基于深度神经网络的雷达辐射源个体识别[D]. 西安: 西安电子科技大学, 2021.
[8] 韩俊, 陈晋汶, 孙茹. 复杂体制雷达辐射源信号识别新方法[J]. 雷达科学与技术, 2016, 14(1): 76-80.
HAN Jun, CHEN Jin-wen, SUN Ru. New method for recognizing complicated radar emitter signal[J]. Radar Science and Technology, 2016, 14(1): 76-80.
[9] CHEN P , LI G, XU K, et al. Applying the frechet distance to the specific emitter identification[C] //2016 IEEE 13th International Conference on Signal Processing (ICSP), 2016.
[10] 陈蒙, 邢小鹏, 陈世文, 等. 基于贝塞尔曲线的雷达信号脉内无意调相特征提取及个体识别[J]. 信息工程大学学报, 2022, 23(1): 9-17.
CHEN Meng, XING Xiao-peng, CHEN Shi-wen, et al. Unintentional phase modulation on pulse feature extraction and radar specific emitter ldentification based on bezier curve[J]. Journal of Information Engineering University, 2022, 23(1): 9-17.
[11] RU X, YE H, LIU Z, et al. An experimental study on secondary radar transponder UMOP characteristics[C]// 2016 European Radar Conference (EuRAD), 2016.
[12] 胡牡华. 支持向量机的舰船图像识别与分类技术[J]. 舰船科学技术, 2022, 44(11): 156-159.
HU Mu-hua. Ship image recognition and classification based on support vector machine[J]. Ship Science and Technology, 2022, 44(11): 156-159.