基于多级箱与深度森林的雷达信号分选算法

doi:10.12263/DZXB.20210934

电子学报 ›› 2022, Vol. 50 ›› Issue (6): 1351-1358.DOI: 10.12263/DZXB.20210934

所属专题：电磁频谱智能+

基于多级箱与深度森林的雷达信号分选算法

张春杰¹^,², 刘俞辰¹^,², 司伟建¹^,²

^1.哈尔滨工程大学信息与通信工程学院，黑龙江哈尔滨 150001
^2.哈尔滨工程大学先进船舶通信与信息技术工业和;信息化部重点实验室，黑龙江哈尔滨 150001

收稿日期:2021-07-16 修回日期:2022-01-07 出版日期:2022-06-25
- 作者简介:
- 张春杰女，1975年出生，黑龙江哈尔滨人.现为哈尔滨工程大学信息与通信工程学院副教授.主要研究方向为宽带信号检测、处理及识别.
  刘俞辰（通讯作者）男，1997年出生，黑龙江哈尔滨人.现为哈尔滨工程大学硕士研究生.主要研究方向为电子侦察、雷达信号分选等.E-mail: liuyuchen11@hrbeu.edu.cn
  司伟建男，1971年出生，黑龙江哈尔滨人.现为哈尔滨工程大学研究员.主要研究方向为阵列信号处理、宽带信号检测、信号分选、数字信道化等.
- 基金资助:
- 国家自然科学基金 (61801143); 黑龙江省自然科学基金 (LH2020F019)

The Radar Signal Deinterleaving Algorithm Based on Multi-Level Bin and Deep Forest

ZHANG Chun-jie¹^,², LIU Yu-chen¹^,², SI Wei-jian¹^,²

^1.College of Information and Communication, Harbin Engineering University, Harbin, Heilongjiang 150001, China
^2.Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin, Heilongjiang 150001, China

Received:2021-07-16 Revised:2022-01-07 Online:2022-06-25 Published:2022-06-25
- Supported by:
- National Natural Science Foundation of China (61801143); Natural Science Foundation of Heilongjiang Province, China (LH2020F019)

摘要/Abstract

摘要：

针对复杂电磁环境下大范围抖动、滑变等特殊类型雷达信号难以分选，脉冲重复周期（Pulse Repetition Interval，PRI）估计精度低，脉冲序列搜索效果不佳等问题，提出一种基于PRI多级箱与深度森林的雷达信号单参数分选算法.该算法利用PRI多级箱结构进行PRI变换，提升针对特殊类型雷达的分选正确率.在多级箱中的脉冲对个数与PRI变换结果中，提取雷达信号PRI边界特征，将不同电磁环境下特殊类型雷达信号PRI边界特征混合，通过平滑滤波器增强特征，训练深度森林预测完整雷达信号PRI范围，从而校正中心PRI估计值.最后依据PRI中心值与变化范围，搜索提取脉冲序列，完成分选.仿真实验表明，所提算法可在复杂电磁环境下，对大范围抖动、单线性滑变、双线性滑变、锯齿波滑变以及正弦滑变等特殊类型雷达信号进行有效分选，PRI范围预测效果在原始深度森林基础上提升14%，PRI估计误差降低75%.

关键词: 电子侦察, 特殊类型雷达信号, 多级箱, 深度森林, PRI估计, PRI范围

Abstract:

To address the difficulty of deinterleaving special radars with large pulse repetition interval(PRI) range and the low accuracy of PRI estimation in complex electromagnetic environment, a radar signal deinterleaving algorithm based on PRI multi-level bin and deep forest is proposed. The algorithm utilizes the PRI multi-level bin structure for PRI transformation to improve the detection rate for special radars. The PRI boundary features of radar signals are derived from the number of pulse pairs in the multi-level bin and the PRI transform results. The PRI boundary features of special radars in different environments is mixed and the features by smoothing filters are enhanced. The deep forest is trained to predict the complete PRI range and thus to correct the central PRI estimate. Finally, based on the central value of PRI and the PRI range, the pulses are searched and extracted. Simulation experiments show that the proposed algorithm can effectively deinterleaving jittered, unilinear, bilinear, sawtooth and sinusoidal radars with large PRI range. The PRI range prediction performance is improved by 14% and the PRI estimation error is reduced by 75%.

Key words: electronic reconnaissance, special radar, multi-level bin, deep forest, pulse repetition interval estimation, pulse repetition interval range

中图分类号:

TN971

张春杰, 刘俞辰, 司伟建. 基于多级箱与深度森林的雷达信号分选算法[J]. 电子学报, 2022, 50(6): 1351-1358.

Chun-jie ZHANG, Yu-chen LIU, Wei-jian SI . The Radar Signal Deinterleaving Algorithm Based on Multi-Level Bin and Deep Forest[J]. Acta Electronica Sinica, 2022, 50(6): 1351-1358.

图/表 12

图1 滑变雷达信号子类型

图2 复杂电磁环境信号分选架构

图3 不同箱变化率下的最佳PRI变换曲线

图4 基于深度森林的PRI边界预测流程图

图5 半PRI箱示意图

图6 平滑滤波增强边界特征结构

图7 平滑滤波增强前后效果对比

图8 深度森林结构图

图9 目标雷达信号分选正确率

图10 左侧PRI边界预测结果平均绝对误差

图11 右侧PRI边界预测结果平均绝对误差

图12 PRI校正前后PRI中心值估计误差对比图

参考文献 21

1	CHENC X, HEM H, XUJ, et al. A new method for sorting unknown radar emitter signal[J]. Chinese Journal of Electronics, 2014, 23(3): 499-502.
2	NAN H, PENGS R, YUJ A, et al. Pulse interference method against PRI sorting[J]. The Journal of Engineering, 2019, 2019(19): 5732-5735.
3	ROGERSJ A V. ESM processor system for high pulse density radar environments[J]. IEE Proceedings F Communications, Radar and Signal Processing, 1985, 132(7): 621-625.
4	AHMEDM G S, TANGB. Sorting radar signal from symmetry clustering perspective[J]. Journal of Systems Engineering and Electronics, 2017, 28(4): 690-696.
5	JIANGW, FUX J, CHANGJ Y. Improved de-interleaving algorithm of radar pulses based on dual fuzzy vigilance ART[J]. Journal of Systems Engineering and Electronics, 2020, 31(2): 303-311.
6	LIUY C, ZHANGQ Y. Improved method for deinterleaving radar signals and estimating PRI values[J]. IET Radar, Sonar & Navigation, 2018, 12(5): 506-514.
7	ZHANGC J, LIUY C, SIW J. Synthetic algorithm for deinterleaving radar signals in a complex environment[J]. IET Radar, Sonar & Navigation, 2020, 14(12): 1918-1928.
8	姜宏志, 赵闯, 胡德秀, 等. 基于时差多参的单脉冲信号实时配对分选[J]. 电子学报, 2021, 49(3): 566-572.
	JIANGH Z, ZHAOC, HUD X, et al. Real-time deinterleaving algorithm for single pulse signal based on TDOAs and multi-parameter information[J]. Acta Electronica Sinica, 2021, 49(3): 566-572. (in Chinese)
9	MARDIAH K. New techniques for the deinterleaving of repetitive sequences[J]. IEE Proceedings F Radar and Signal Processing, 1989, 136(4): 149-154.
10	MILOJEVIĆD J, POPOVIĆB M. Improved algorithm for the deinterleaving of radar pulses[J]. IEE Proceedings F Radar and Signal Processing, 1992, 139(1): 98-104.
11	XIAOW H, WUH C, YANGC Z, et al. An improved SDIF radar pulse signal main sorting algorithm[J]. Advanced Materials Research, 2013, 710: 637-641.
12	BAGHERIM, SEDAAGHIM H. A new approach to pulse deinterleaving based on adaptive thresholding[J]. Turkish Journal of Electrical Engineering & Computer Sciences, 2017, 25(5): 3827-3838.
13	NISHIGUCHIK, KOBAYASHIM. Improved algorithm for estimating pulse repetition intervals[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(2): 407-421.
14	MAOY, HANJ, GUOG H, et al. An improved algorithm of PRI transform[C]//2009 WRI Global Congress on Intelligent Systems. Xiamen: IEEE, 2009: 145-149.
15	JIANGH Z, ZHAOC, ZHAOY J. Coherent integration algorithm for frequency-agile and PRF-jittering signals in passive localization[J]. Chinese Journal of Electronics, 2021, 30(4): 781-792.
16	陈涛, 王天航, 郭立民. 基于PRI变换的雷达脉冲序列搜索方法[J]. 系统工程与电子技术, 2017, 39(6): 1261-1267.
	CHENT, WANGT H, GUOL M. Sequence searching methods of radar signal pulses based on PRI transform algorithm[J]. Systems Engineering and Electronics, 2017, 39(6): 1261-1267. (in Chinese)
17	秦鑫, 黄洁, 查雄, 等. 基于扩张残差网络的雷达辐射源信号识别[J]. 电子学报, 2020, 48(3): 456-462.
	QINX, HUANGJ, ZHAX, et al. Radar emitter signal recognition based on dilated residual network[J]. Acta Electronica Sinica, 2020, 48(3): 456-462. (in Chinese)
18	潘剑飞, 曹燕, 董一鸿, 等. 基于Attention深度随机森林的社区演化事件预测[J]. 电子学报, 2019, 47(10): 2050-2060.
	PANJ F, CAOY, DONGY H, et al. The community evolution event prediction based on attention deep random forest[J]. Acta Electronica Sinica, 2019, 47(10): 2050-2060. (in Chinese)
19	ZHOUZ H, FENGJ. Deep forest: Towards an alternative to deep neural networks[C]//Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence Main track. California: IJCAI, 2017: 3553-3559.
20	张亮, 王国宏, 张翔宇, 等. LFM雷达对抗移频干扰方法研究[J]. 电子学报, 2021, 49(3): 510-517.
	ZHANGL, WANGG H, ZHANGX Y, et al. The research on LFM radar countering frequency-shift jamming methods[J]. Acta Electronica Sinica, 2021, 49(3): 510-517. (in Chinese)
21	TANGJ, XIAH, ZHANGJ, et al. Deep forest regression based on cross-layer full connection[J]. Neural Computing and Applications, 2021, 33(15): 9307-9328.

[1]	姜宏志, 赵闯, 胡德秀, 赵拥军, 赵勇胜, 刘智鑫. 基于时差多参的单脉冲信号实时配对分选[J]. 电子学报, 2021, 49(3): 566-572.
[2]	王壮, 樊昀, 王成, 康少单, 孙兆林, 胡卫东, 郁文贤. 基于星载电子侦察与成像侦察的数据融合技术[J]. 电子学报, 2003, 31(S1): 2127-2130.

基于多级箱与深度森林的雷达信号分选算法

The Radar Signal Deinterleaving Algorithm Based on Multi-Level Bin and Deep Forest

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 21

相关文章 2

编辑推荐

Metrics