基于近似熵的Multi-h CPM调制识别算法

doi:10.3969/j.issn.1001-506X.2020.03.026

摘要/Abstract

摘要：

Multi-h连续相位调制(continuous phase modulation, CPM)信号与其调制指数均值相等的Single-h CPM信号的特征具有极大相似性,难以区分。针对该问题,提出了一种基于近似熵的Multi-h CPM调制识别算法。该算法将信号按照相同调制指数为一组的方式拆分为多个子序列,通过舍弃符号间拼接产生的多余模式向量对近似熵进行修正,然后利用Multi-h CPM信号各子序列近似熵的差异性,完成Multi-h CPM信号和Single-h CPM信号的类间识别,最后利用概率神经网络完成类内识别。实验结果表明,该算法在信噪比低至11 dB时,仍可以达到90%的识别率。

关键词: Multi-h连续相位调制, 调制识别, 近似熵, 概率神经网络

Abstract:

Multi-h continuous phase modulation (CPM) signal is indistinguishable from Single-h CPM signal, when the latter's modulation index is equal to the former's average of modulation indices, for their characteristics are similar. Aiming at this problem, a Multi-h CPM modulation recognition algorithm based on approximate entropy is proposed. The algorithm splits the complete sequence of signal into multiple sub-sequences according to the same modulation index, and discards the extra pattern vectors generated by splicing between symbols, to modify the approximate entropy. Then it uses the difference of entropy between subsequences of the Multi-h CPM signal, the inter-class recognition of Multi-h CPM signal and Single-h CPM signal is completed. Finally, the intra-class identification is accomplished by using probabilistic neural networks. The experimental results show that the algorithm can achieve 90% recognition rate when the signal-to-noise ratio is as low as 11 dB.

Key words: Multi-h continuous phase modulation (CPM), modulation recognition, approximate entropy, probabilistic neural networks

中图分类号:

TN911.3

刘凯, 赵梦伟, 黄青华. 基于近似熵的Multi-h CPM调制识别算法[J]. 系统工程与电子技术, 2020, 42(3): 698-703.

Kai LIU, Mengwei ZHAO, Qinghua HUANG. Multi-h CPM modulation recognition algorithm based on approximate entropy[J]. Systems Engineering and Electronics, 2020, 42(3): 698-703.

图/表 12

图1

表1

图2

表2

图3

图4

表3

表4

图5

图6

图7

图8

参考文献 29

1	SUNDBERG C E . Continuous phase modulation[J]. IEEE Communications Magazine, 1986, 24 (4): 25- 38. doi: 10.1109/MCOM.1986.1093063
2	ZHANG J , WANG F , ZHONG Z , et al. Continuous phase modulation classification via Baum-Welch algorithm[J]. IEEE Communications Letters, 2018, 22 (7): 1390- 1393. doi: 10.1109/LCOMM.2018.2821171
3	MUNAWAR T , SALEEM S , HASSAN S A , et al. Estimation of modulation index for partial response CPM signal[J]. IEEE Access, 2018, 6, 7664- 7674. doi: 10.1109/ACCESS.2018.2794959
4	SEMENOV V Y . Demodulation method for continuous phase modulation signals based on least-squares method[J]. Radioelectronics and Communications Systems, 2018, 61 (4): 153- 156. doi: 10.3103/S0735272718040027
5	RAMIREZPEREZ A , PARRAMICHEL R , RODRIGUEZGARCIA A , et al. A new Single-h and Multi-h CPM transmitter[J]. IEEE Trans.on Circuits & Systems I Regular Papers, 2017, 63 (5): 716- 726.
6	SALEEM S , STUBER G L . Frequency-domain equalization techniques for Multi-h continuous phase modulation[J]. IEEE Trans.on Communications, 2014, 62 (6): 1818- 1829. doi: 10.1109/TCOMM.2014.2316815
7	HUCKELL G R, TIRPAK F M, CHANDLER E W. A new layered protocol integrating 5-kHz and 25-kHz DAMA operations: a proposed improvement to the UHF DAMA standards[C]//Proc.of the IEEE Military Communications Conference, 1999: 1333-1337.
8	VOGLEWEDE P E. Frequency hopping with multih CPM (MIL-STD-188-181B)[C]//Proc.of the IEEE Military Communications Conference, 2003: 1089-1094.
9	DOBRE O A , ABDI A , BARNESS Y , et al. Survey of automatic modulation classification techniques: classical approaches and new trends[J]. IET Communications, 2007, 1 (2): 137- 156. doi: 10.1049/iet-com:20050176
10	BIANCHI P , LOUBATON P , SIRVEN F . Non data-aided estimation of the modulation index of continuous phase modulations[J]. IEEE Trans.on Signal Processing, 2014, 52 (10): 2847- 2861.
11	DECHEN X U , ZHANG Y . Estimation of the modulation index of CPM signals based on Laurent's decomposition[J]. IEEE Trans.on Wireless Communications, 2013, 12 (12): 6268- 6280. doi: 10.1109/TWC.2013.101713.130237
12	PAWAR S U , DOHERTY J F . Modulation recognition in continuous phase modulation using approximate entropy[J]. IEEE Trans.on Information Forensics Security, 2011, 6 (3): 843- 852. doi: 10.1109/TIFS.2011.2159000
13	吴斌, 袁亚博, 汪勃. 基于记忆因子的连续相位调制信号最大似然调制识别[J]. 电子与信息学报, 2016, 38 (10): 2546- 2552.
	WU B , YUAN Y B , WANG B . Maximum likelihood modulation recognition for continuous phase modulation signals using memory factor[J]. Journal of Electronics & Information Technology, 2016, 38 (10): 2546- 2552.
14	上官泽胤. Multi-h CPM信号的参数设计研究[J]. 遥测遥控, 2018, 39 (5): 70- 76.
	SHANGGUAN Z Y . Research on parameters design of Multi-h CPM signal[J]. Journal of Telemetry, Tracking and Command, 2018, 39 (5): 70- 76.
15	刘璐. Multi-h CPM低复杂度解调方法研究[D].长沙:国防科技大学, 2012.
	LIU L.The research of low-complexity demodulation for Multi-h CPM[D]. Changsha: National University of Defense Technology, 2012.
16	刘源, 王世练, 张炜, 等. 基于近似熵的Multi-h CPM信号调制方式识别[J]. 通信对抗, 2015, 34 (2): 14- 17.
	LIU Y , WANG S L , ZHANG W , et al. Modulation recognition of Multi-h CPM signal based on approximate entropy[J]. Communication Countermeasures, 2015, 34 (2): 14- 17.
17	SPECHT D F . Probabilistic neural networks[J]. Neural Networks, 1990, 3 (1): 109- 118. doi: 10.1016/0893-6080(90)90049-Q
18	PINCUS , S M . Approximate entropy as a measure of system complexity[J]. Proceedings of the National Academy of Sciences of the United States of America, 1991, 88 (6): 2297- 2301. doi: 10.1073/pnas.88.6.2297
19	CHAN H L , LIN L Y , LIN M A , et al. Nonlinear characteristics of heart rate variability during unsupervised and steady physical activities[J]. Physiological Measurement, 2007, 28 (3): 277- 286. doi: 10.1088/0967-3334/28/3/004
20	GABRIEL R , ABIGAIL M , REMCO C , et al. At a glance: pixel approximate entropy as a measure of line chart complexity[J]. IEEE Trans.on Visualization and Computer Graphics, 201, 25 (1): 872- 881.
21	MONTESINOS L , CASTALDO R , PECCHIA L . On the use of approximate entropy and sample entropy with centre of pressure time-series[J]. Journal of Neuroengineering and Rehabilitation, 2018, 15 (1): 116- 121. doi: 10.1186/s12984-018-0465-9
22	PINCUS S M , VISCARELLO R R . Approximate entropy: a regularity measure for fetal heart rate analysis[J]. Obstetrics & Gynecology, 1992, 79 (2): 249- 255.
23	DAWES G S , MOULDEN M , SHEIL O , et al. Approximate entropy, a statistic of regularity, applied to fetal heart rate data before and during labor[J]. Obstetrics and Gynecology, 1992, 80 (5): 763- 768.
24	PINCUS S M , GLADSTONE I M , EHRENKRANZ R A . A regularity statistic for medical data analysis[J]. Journal of Clinical Monitoring, 1991, 7 (4): 335- 345. doi: 10.1007/BF01619355
25	JEONG C M , JUNG Y G , LEE S J . Neural network-based radar signal classification system using probability moment and ApEn[J]. Soft Computing, 2017, 22 (13): 4205- 4219.
26	CHEN W , ZHUANG J , YU W , et al. Measuring complexity using FuzzyEn, ApEn, and SampEn[J]. Medical Engineering and Physics, 2009, 31 (1): 61- 68.
27	YANG X , CHEN W , LI A , et al. BA-PNN-based methods for power transformer fault diagnosis[J]. Advanced Engineering Informatics, 2019, 39 (1): 178- 185.
28	RICHMAN J , MOORMAN J . Physiological time-series analysis, using approximate entropy and sample entropy[J]. American Journal of Physiology-Heart and Circulatory Physiology, 2000, 278 (6): 2039- 2049. doi: 10.1152/ajpheart.2000.278.6.H2039
29	LI Y , PAN W , LI K , et al. Sliding trend fuzzy approximate entropy as a novel descriptor of heart rate variability in obstructive sleep apnea[J]. IEEE Journal of Biomedical and Health Informatics, 2018, 23 (1): 175- 183.

调制指数	全序列	奇序列	偶序列
[5/16]	[0.09, 0.09]	-	-
[5/16, 6/16]	[0.10, 0.10]	[0.25, 0.25]	[0.27, 0.27]
[11/32]	[0.10, 0.10]	[0.26, 0.26]	[0.26, 0.26]
[6/16]	[0.11, 0.11]	-	-

调制指数	全序列	奇序列	偶序列
[5/16]	[0.09, 0.09]	-	-
[5/16, 6/16]	[0.10, 0.10]	[0.09, 0.09]	[0.11, 0.11]
[11/32]	[0.10, 0.10]	[0.10, 0.10]	[0.10, 0.10]
[6/16]	[0.11, 0.11]	-	-

信号集	Multi-h CPM信号	Single-h CPM信号
A	h=[5/16, 6/16]	h=[11/32]
B	h=[6/16, 7/16]	h=[13/32]
C	h=[7/16, 10/16]	h=[17/32]
D	h=[12/16, 13/16]	h=[25/32]

参数	值
关联长度	L=1
进制数	M=2
脉冲波形	q(t)=LRC
采样率	N_s=8
模式维度	m=2
容错阈值系数	c=0.8

[1]	康颖, 赵治华, 吴灏, 李亚星, 孟进. 基于Deep SVDD的通信信号异常检测方法[J]. 系统工程与电子技术, 2022, 44(7): 2319-2328.
[2]	秦博伟, 蒋磊, 许华, 齐子森. 基于残差生成对抗网络的调制识别算法[J]. 系统工程与电子技术, 2022, 44(6): 2019-2026.
[3]	邵凯, 朱苗苗, 王光宇. 基于生成对抗与卷积神经网络的调制识别方法[J]. 系统工程与电子技术, 2022, 44(3): 1036-1043.
[4]	史蕴豪, 许华, 郑万泽, 刘英辉. 基于集成学习与特征降维的小样本调制识别方法[J]. 系统工程与电子技术, 2021, 43(4): 1099-1109.
[5]	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.
[6]	李晨, 杨俊安, 刘辉. 基于信息熵和GA-ELM的调制识别算法[J]. 系统工程与电子技术, 2020, 42(1): 223-229.
[7]	吴灏, 周亮, 李亚星, 郭宇, 孟进. 基于卷积神经网络和稀疏滤波的调制识别方法[J]. 系统工程与电子技术, 2019, 41(9): 2114-2121.
[8]	郝云飞, 刘章孟, 郭福成, 张敏. 基于生成对抗网络的信号调制方式的开集识别[J]. 系统工程与电子技术, 2019, 41(11): 2619-2624.
[9]	谭晓衡, 褚国星, 张雪静, 杨扬. 基于高阶累积量和小波变换的调制识别算法[J]. 系统工程与电子技术, 2018, 40(1): 171-177.
[10]	付俊强, 李蓉, 赵成林, 李斌. 基于贝叶斯序贯推理的自适应调制识别算法[J]. 系统工程与电子技术, 2015, 37(12): 2860-2864.
[11]	靳晓艳，周希元. 一种最大似然调制识别的快速算法[J]. Journal of Systems Engineering and Electronics, 2013, 35(3): 615-618.
[12]	杨发权，李赞，李红艳，郝本建，潘忠显. 基于蜂群算法和神经网络的通信调制识别方法[J]. 系统工程与电子技术, 2013, 35(10): 2186-2191.
[13]	李一兵, 葛娟, 林云. 基于熵特征和支持向量机的调制识别方法[J]. Journal of Systems Engineering and Electronics, 2012, 34(8): 1691-1695.
[14]	谭晓衡, 陈印. 基于联合特征参数的数字调制识别优化算法[J]. Journal of Systems Engineering and Electronics, 2011, 33(12): 2732-2736.
[15]	程汉文1，陈亮2，吴乐南3. 基于加权表决的决策层融合多系统调制识别[J]. Journal of Systems Engineering and Electronics, 2010, 32(2): 342-345.