基于长短时记忆网络的雷达波形设计

doi:10.12305/j.issn.1001-506X.2021.02.12

系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (2): 376-382.doi: 10.12305/j.issn.1001-506X.2021.02.12

基于长短时记忆网络的雷达波形设计

赵俊龙¹(), 李伟¹(), 王泓霖²(), 黄腾³(), 甘奕夫¹(), 王也⁴()

1. 空军工程大学信息与导航学院, 陕西西安710077
2. 中国人民解放军95851部队, 上海 201900
3. 广州大学计算机科学与网络工程学院, 广东广州 510006
⁴ 中国人民解放军95939部队, 河北沧州 061722 Unit 95939 of the PLA, Cangzhou 061722, China

收稿日期:2020-04-28 出版日期:2021-02-01 发布日期:2021-03-16
作者简介:赵俊龙(1995-),男,硕士研究生,主要研究方向为深度学习、雷达波形设计。E-mail:jlzhao0826@163.com|李伟(1978-),男,副教授,硕士研究生导师，博士,主要研究方向为新体制雷达、雷达波形设计。E-mail:liweichangsha@163.com|王泓霖(1995-),男,工程师,硕士,主要研究方向为博弈论、雷达波形设计。E-mail:wanghonglin821@outlook.com|黄腾(1985-),男,讲师,博士,主要研究方向为人工智能、雷达信号处理。E-mail:huangteng1220@buaa.edu.cn|甘奕夫(1998-),男,硕士研究生,主要研究方向为深度学习、雷达波形设计。E-mail:1922247521@qq.com|王也(1994-),男,工程师,硕士,主要研究方向为机器学习、短波通信。E-mail:694540459@qq.com
基金资助:
国家自然科学基金(61571456);航空科学基金(20160196001);陕西省自然科学基金(2020JM-347)

Waveform design of radar based on long short-term memory network

Junlong ZHAO¹(), Wei LI¹(), Honglin WANG²(), Teng HUANG³(), Yifu GAN¹(), Ye WANG⁴()

1. Information and Navigation College, Air Force Engineering University, Xi’an 710077, China
2. Unit 95851 of the PLA,Shanghai 201900, China
3. School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China

Received:2020-04-28 Online:2021-02-01 Published:2021-03-16

摘要/Abstract

摘要：

针对单准则设计的波形难以满足雷达多工作模式和多任务问题,联合互信息(mutual information, MI)准则和信杂噪比(signal to clutter and noise ratio, SCNR)准则,提出一种基于长短时记忆(long short-term memory, LSTM)网络的雷达波形设计方法。首先，设计了由输入层、LSTM层和输出层构成的LSTM网络。其次，将基于MI准则和SCNR准则生成的信号与相应环境信息组成训练集,训练LSTM网络。最后,使用训练完成的LSTM网络设计波形,并将所提方法生成波形的雷达性能与单准则设计的波形进行对比。为衡量雷达综合性能,提出一种新的雷达综合性能指标和目标识别率。仿真结果表明,所提方法生成信号作为发射信号时,与MI准则生成信号相比,雷达综合性能平均提升0.67%,与SCNR准则相比,平均提升1.47%,证明了该方法可兼具MI准则和SCNR准则优点,提升雷达综合性能。

关键词: 波形设计, 互信息准则, 信杂噪比准则, 长短时记忆网络

Abstract:

Considering the problem that the waveform designed using single-criterion is difficult to meet the radar's multi-working mode and multi-tasking, a waveform design method based on long short-term memory (LSTM) network is proposed, through combining mutual information (MI) criterion and signal to clutter and noise ratio (SCNR) criterion. Firstly, a LSTM network composed of input layer, LSTM layer, and output layer is designed. Secondly, a train set composed of signals generated through MI criteria and SCNR criteria and the corresponding environmental information is constructed, which is used to train the LSTM network. Finally, the trained LSTM network is used to design waveform, and the radar performance of the waveform generated by the proposed method is compared with the single-criterion design waveform. In order to measure the comprehensive performance of radar, a novel comprehensive performance indicator and target recognition rate of radar are proposed. Simulation results show that when the signal generated by the proposed method is used as the transmit signal, the comprehensive performance of radar is increased by 0.67% on average compared with MI criterion and 1.47% compared with SCNR criterion. It is proved that the proposed method can combine the advantages of the MI criterion and SCNR criterion, and improve the comprehensive performance of radar.

Key words: waveform design, mutual information (MI) criterion, signal to clutter and noise ratio (SCNR) criterion, long short-term memory network (LSTM)

中图分类号:

TN951

赵俊龙, 李伟, 王泓霖, 黄腾, 甘奕夫, 王也. 基于长短时记忆网络的雷达波形设计[J]. 系统工程与电子技术, 2021, 43(2): 376-382.

Junlong ZHAO, Wei LI, Honglin WANG, Teng HUANG, Yifu GAN, Ye WANG. Waveform design of radar based on long short-term memory network[J]. Systems Engineering and Electronics, 2021, 43(2): 376-382.

图/表 9

图1

图2

图3

图4

图5

图6

图7

图8

表1

参考文献 31

1	GAO F , HUANG T , WANG J , et al. A novel multi-input bidirectional LSTM and HMM based approach for target recognition from multi-domain radar range profiles[J]. Electronics, 2019, 8 (5): 535- 553. doi: 10.3390/electronics8050535
2	HAYKIN S . Cognitive radar:a way of the future[J]. IEEE Signal Processing Magazine, 2006, 23 (1): 30- 40. doi: 10.1109/MSP.2006.1593335
3	WANG L, ZHANG Y L, LIAO Q M, et al.Robust waveform design for multi-target detection in cognitive MIMO radar[C]//Proc.of the IEEE Radar Conference, 2018: 116-120.
4	崔国龙, 余显祥, 杨婧, 等. 认知雷达波形优化设计方法综述[J]. 雷达学报, 2019, 8 (5): 537- 557.
	CUI G L , YU X X , YANG J , et al. An overview of waveform optimization methods for cognitive radar[J]. Journal of Radars, 2019, 8 (5): 537- 557.
5	XU M, DU N, LI Z Y, et al.Cognitive SAR waveform design method based on joint optimization criteria[C]//Proc.of the 6th Asia-Pacific Conference on Synthetic Aperture Radar, 2019.
6	BELL M R . Information theory and radar waveform design[J]. IEEE Trans.on Information Theory, 1993, 39 (5): 1578- 1597. doi: 10.1109/18.259642
7	GUO D , SHAMAI S , VERDU S . Mutual information and minimum mean-square error in Gaussian channels[J]. IEEE Trans.on Information Theory, 2005, 51 (4): 1261- 1282. doi: 10.1109/TIT.2005.844072
8	YANG Y , BLUM R S . MIMO radar waveform design based on mutual information and minimum mean-square error estimation[J]. IEEE Trans.on Aerospace and Electronic Systems, 2007, 43 (1): 330- 343. doi: 10.1109/TAES.2007.357137
9	GRIEVE P G, GUERCI J R.Optimum matched illumination-reception radar: US5175552[P].1992-12-29.
10	PILLAI S U , YOULA D C , OH H S , et al. Optimum transmit-receiver design in the presence of signal-dependent interference and channel noise[J]. IEEE Trans.on Information Theory, 2002, 46 (5): 577- 584.
11	ROMERO R A , BAE J , GOODMAN N A . Theory and application of SNR and mutual information matched illumination waveforms[J]. IEEE Trans.on Aerospace and Electronic Systems, 2011, 47 (2): 912- 927. doi: 10.1109/TAES.2011.5751234
12	HAYKIN S, XUE Y, DAVIDSON T N.Optimal waveform design for cognitive radar[C]//Proc.of the 42nd Asilomar Conference on Signals, Systems and Computers, 2008.
13	ZHANG J D, ZHU D Y, ZHANG G.Multi-objective waveform design for cognitive radar[C]//Proc.of the IEEE CIE International Conference on Radar, 2011: 580-583.
14	ZHAO W , JIAO L C , MA W P , et al. Superpixel-based multiple local CNN for panchromatic and multispectral image classification[J]. IEEE Trans.on Geoscience and Remote Sensing, 2017, 55 (7): 4141- 4156. doi: 10.1109/TGRS.2017.2689018
15	NISHIMURA Y , SUDOH K , NEUBIG G , et al. Multi-source neural machine translation with missing data[J]. IEEE/ACM Trans.on Audio, Speech, and Language Processing, 2020, 28, 569- 580. doi: 10.1109/TASLP.2019.2959224
16	LECUN Y , BENGIO Y , HINTON G E , et al. Deep learning[J]. Nature, 2015, 521 (7553): 436- 444. doi: 10.1038/nature14539
17	YE H , LI G Y , JUANG B H F . Power of deep learning for channel estimation and signal detection in OFDM systems[J]. IEEE Wireless Communications Letters, 2017, 7 (1): 114- 117.
18	O'SHEA T J , HPYDIS J . An introduction to deep learning for the physical layer[J]. IEEE Trans.on Cognitive Communcations & Networking, 2017, 3 (4): 563- 575.
19	HUIZING A , HEILIGERS M , DEKKER B , et al. Deep learning for classification of mini-UAVs using micro-Doppler spectrograms in cognitive radar[J]. IEEE Aerospace and Electronic Systems Magazine, 2019, 34 (11): 46- 56. doi: 10.1109/MAES.2019.2933972
20	李健伟, 曲长文, 彭书娟, 等. 基于卷积神经网络的SAR图像舰船目标检测[J]. 系统工程与电子技术, 2018, 40 (9): 1953- 1959.
	LI J W , QU C W , PENG S J , et al. Ship detection in SAR images based on convolutional neural network[J]. Systems Engineering and Electronics, 2018, 40 (9): 1953- 1959.
21	WANG H P, CHEN S Z, XU F, et al.Application of deep-learning algorithms to MSTAR data[C]//Proc.of the IEEE International Geoscience and Remote Sensing Symposium, 2015: 3743-3745.
22	WU S N, WANG K, OUYANG Y W.Research on ground object classification detection method of heterologous SAR image based on transfer learning[C]//Proc.of the IEEE 8th Joint International Information Technology and Artificial Intelligence Conference, 2019: 1496-1499.
23	DAI S Z , LI L , LI Z H . Modeling vehicle interactions via modi-fied LSTM models for trajectory prediction[J]. IEEE Access, 2019, 7, 38287- 38296. doi: 10.1109/ACCESS.2019.2907000
24	XIE Y , LIANG R Y , LIANG Z L , et al. Speech emotion classi-fication using attention-based LSTM[J]. IEEE/ACM Trans.on Audio, Speech, and Language Processing, 2019, 27 (11): 1675- 1685. doi: 10.1109/TASLP.2019.2925934
25	SHIN Y , LEE S G . Learning context using segment-level LSTM for neural sequence labeling[J]. IEEE/ACM Trans.on Audio, Speech, and Language Processing, 2020, 28, 105- 115. doi: 10.1109/TASLP.2019.2948773
26	黎湘, 范梅梅. 认知雷达及其关键技术研究进展[J]. 电子学报, 2012, 40 (9): 1863- 1870. doi: 10.3969/j.issn.0372-2112.2012.09.025
	LI X , FAN M M . Research advance on cognitive radar and its key technology[J]. Acta Electronica Sinica, 2012, 40 (9): 1863- 1870. doi: 10.3969/j.issn.0372-2112.2012.09.025
27	赵树杰, 赵建勋. 信号检测与估计理论[M]. 北京: 清华大学出版社, 2005.
	ZHAO S J , ZHAO J X . Theory of signal detection and estimation[M]. Beijing: Tsinghua University Press, 2005.
28	李伟, 王泓霖, 郑家毅, 等. 博弈条件下雷达波形设计策略研究[J]. 电子与信息学报, 2019, 41 (11): 2654- 2660. doi: 10.11999/JEIT190114
	LI W , WANG H L , ZHENG J Y , et al. Research on radar waveform design strategy under game condition[J]. Journal of Electronics and Information Technology, 2019, 41 (11): 2654- 2660. doi: 10.11999/JEIT190114
29	GRAVES A . Supervised sequence labelling with recurrent neural networks[M]. Berlin: Springer Press, 2012.
30	HAN J H , LIU H , WANG M Y , et al. ERA-LSTM:an efficient ReRAM-based architecture for long short-term memory[J]. IEEE Trans.on Parallel and Distributed Systems, 2020, 31 (6): 1328- 1342. doi: 10.1109/TPDS.2019.2962806
31	HABIBUR R . Fundamental principles of radar[M]. Florida: CRC Press, 2019.

类型	P_d	P_r	P_c
LSTM网络	0.444 3	0.968 7	0.430 4
MI准则	0.423 7	1.000 0	0.423 7
SCNR准则	0.568 4	0.731 4	0.415 7

[1]	肖宇, 邓正宏, 张展. 基于双阶段互信息准则的多目标检测波形设计[J]. 系统工程与电子技术, 2022, 44(9): 2736-2742.
[2]	吉瑞萍, 张程祎, 梁彦, 王跃东. 基于LSTM的弹道导弹主动段轨迹预报[J]. 系统工程与电子技术, 2022, 44(6): 1968-1976.
[3]	侯召国, 王华伟, 周良, 付强. 基于改进深度残差网络的旋转机械故障诊断[J]. 系统工程与电子技术, 2022, 44(6): 2051-2059.
[4]	张彦, 叶春茂, 李璋峰, 鲁耀兵. 多种转发干扰下的脉内脉间波形综合优化设计[J]. 系统工程与电子技术, 2022, 44(5): 1495-1501.
[5]	夏栋, 张凯旋, 丁友宝, 李宝鹏. 基于相位编码波形捷变和CFAR技术的抗同频干扰[J]. 系统工程与电子技术, 2022, 44(4): 1210-1219.
[6]	孙世岩, 张钢, 梁伟阁, 佘博, 田福庆. 基于时间序列数据扩增和BLSTM的滚动轴承剩余寿命预测方法[J]. 系统工程与电子技术, 2022, 44(3): 1060-1068.
[7]	曹亚丽, 李梅梅, 屈诗涵, 宋昕. 联合准则下的认知雷达波形设计[J]. 系统工程与电子技术, 2022, 44(11): 3364-3370.
[8]	何金阳, 程子扬, 何子述. 抗间歇采样转发干扰的认知恒模波形设计方法[J]. 系统工程与电子技术, 2021, 43(9): 2448-2456.
[9]	舒涛, 张一弛, 丁日显. 基于灰色模型与LSTM网络的旋转机械轴承寿命预测[J]. 系统工程与电子技术, 2021, 43(8): 2355-2361.
[10]	肖宇, 邓正宏. 信杂噪比约束下基于互信息的雷达波形设计[J]. 系统工程与电子技术, 2021, 43(7): 1775-1780.
[11]	时晨光, 董璟, 周建江, 汪飞. 飞行器射频隐身技术研究综述[J]. 系统工程与电子技术, 2021, 43(6): 1452-1467.
[12]	张彦, 叶春茂, 鲁耀兵, 陈学斌. 双曲调频波形抑制间歇采样转发干扰效果分析[J]. 系统工程与电子技术, 2021, 43(11): 3169-3176.
[13]	张一凡, 张双辉, 刘永祥, 荆锋. 基于注意力机制的堆叠LSTM网络雷达HRRP序列目标识别方法[J]. 系统工程与电子技术, 2021, 43(10): 2775-2781.
[14]	刘凯, 张斌, 黄青华. 基于TCNN-BiLSTM网络的调制识别算法[J]. 系统工程与电子技术, 2020, 42(8): 1841-1849.
[15]	李京峰, 陈云翔, 项华春, 蔡忠义. 基于LSTM-DBN的航空发动机剩余寿命预测[J]. 系统工程与电子技术, 2020, 42(7): 1637-1644.

基于长短时记忆网络的雷达波形设计

Waveform design of radar based on long short-term memory network

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 31

相关文章 15

编辑推荐

Metrics

本文评价