基于特征值的动态数字信道化子带检测算法

doi:10.12305/j.issn.1001-506X.2024.05.33

摘要/Abstract

摘要：

动态数字信道化接收结构通过检测所有子带, 判断信号是否存在, 为综合滤波器组处理提供依据, 因此子带检测在接收结构中起着关键作用。针对传统检测算法存在低信噪比(signal-to-noise ratio, SNR)下检测性能不高的问题, 依据随机矩阵理论, 提出了基于最大最小特征值之差与平均特征值之比的检测算法, 利用平均特征值和最小特征值的极限分布规律来推导算法的检测门限。其次, 根据所有子带数据获取的特征值信息对所提算法进行了优化。最后, 在动态数字信道化接收结构中, 分析不同因素下算法的性能, 表明了所提算法能够克服低SNR的影响, 子带检测的性能更好。

关键词: 动态数字信道化接收, 子带信号检测, 随机矩阵, 特征值检测

Abstract:

The dynamic digital channelized receiving structure detects all sub-bands to determine if there is a signal. The detection results provide a basis for processing the integrated filter bank, so it plays a vital role in the receiving structure. Since the poor performance of the traditional detection algorithm under low signal-to-noise ratio(SNR), a novel detection algorithm based on the ratio of the difference between the maximum and minimum eigen values to the average eigenvalue is proposed according to the random matrix theory. The detection threshold of the algorithm is derived by using the limit distribution law of the average eigenvalue and the minimum eigenvalue. Then, the algorithm is optimized according to the eigenvalue information obtained from all sub-band data. Finally, the performance of the proposed algorithm under different factors is analyzed in the dynamic digital channelized receiving structure, which shows that the algorithm can overcome the effects of the low SNR and perform better in the sub-band detection process.

Key words: dynamic digital channelized receive, sub-band signal detection, random matrix, eigenvalue detection

中图分类号:

TN911

李晓辉, 万宏杰, 石明利, 王先文. 基于特征值的动态数字信道化子带检测算法[J]. 系统工程与电子技术, 2024, 46(5): 1801-1809.

Xiaohui LI, Hongjie WAN, Mingli SHI, Xianwen WANG. Dynamic digital channelized sub-band detection algorithm based on eigenvalue distribution[J]. Systems Engineering and Electronics, 2024, 46(5): 1801-1809.

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参考文献 30

1	WANG L, WANG K D, ZHAO R Q, et al. An efficient real-time digital channelization method based on frequency-domain windowing[C]//Proc. of the 4th International Conference on Information Communication and Signal Processing, 2021: 533-537.
2	GHELFI P , SCOTTI F , ONORI D , et al. Photonics for ultrawideband RF spectral analysis in electronic warfare applications[J]. IEEE Journal of Selected Topics in Quantum Electronics, 2019, 25 (4): 8900209.
3	程翔, 王轩. 复杂电磁环境构建及效能评估发展与构想[J]. 雷达与对抗, 2021, 41 (4): 11-14, 19.
	CHENG X , WANG X . Development and conception of construction of complex electromagnetic environment and efficiency eva-luation[J]. Radar & ECM, 2021, 41 (4): 11-14, 19.
4	廖红舒. 通信侦察信号处理关键技术研究[D]. 成都: 电子科技大学, 2011.
	LIAO H S. A study of key technologies of signal processing for communication reconnaissance[D]. Chengdu: University of Electronic Science and Technology of China, 2011.
5	ZHANG J J , LI J . RF channelization technology[M]. Cham: Springer International Publishing, 2022.
6	胡君朋. 宽带无线信号侦测中的动态信道化技术研究[D]. 长沙: 国防科技大学, 2016.
	HU J P. Research on dynamic channelization technique for broadband wireless signal reconnaissance[D]. Changsha: National University of Defense Technology, 2016.
7	ARJOUNE Y, MRABET Z E, GHAZI H E, et al. Spectrum sensing: enhanced energy detection technique based on noise measurement[C]//Proc. of the IEEE 8th Annual Computing and Communication Workshop and Conference, 2018: 828-834.
8	SHERBIN M K, SINDHU V. Cyclostationary feature detection for spectrum sensing in cognitive radio network[C]//Proc. of the International Conference on Intelligent Computing and Control Systems, 2019: 1250-1254.
9	AWIN F , ABDEL-RAHEEM E , TEPE K . Blind spectrum sensing approaches for interweaved cognitive radio system: a tutorial and short course[J]. IEEE Communications Surveys Tutorials, 2019, 21 (1): 238- 259. doi: 10.1109/COMST.2018.2863681
10	YUCEK T , ARSLAN H . A survey of spectrum sensing algorithms for cognitive radio applications[J]. IEEE Communications Surveys Tutorials, 2009, 11 (1): 116- 130. doi: 10.1109/SURV.2009.090109
11	TAO T . Topics in radom matrix theory[M]. Rhode: American Mathematical Society, 2012.
12	MARCENKO V A , PASTUR L A . Distribution of eigenvalues for some sets of random matrices[J]. Mathematics of the USSR-Sbornik, 1967, 114 (4): 507- 536.
13	ZENG Y H, LIANG Y C. Maximum-minimum eigenvalue detection for cognitive radio[C]//Proc. of the 18th International Symposium on Personal, Indoor and Mobile Radio Communications, 2007.
14	CHAURASIYA R B , SHRESTHA R . Hardware-efficient and fast sensing-time maximum-minimum-eigenvalue-based spectrum sensor for cognitive radio network[J]. IEEE Trans.on Circuits and Systems Ⅰ: Regular Papers, 2019, 66 (11): 4448- 4461. doi: 10.1109/TCSI.2019.2921831
15	ZENG Y , LIANG Y C . Eigenvalue-based spectrum sensing algorithms for cognitive radio[J]. IEEE Trans.on Communications, 2009, 57 (6): 1784- 1793. doi: 10.1109/TCOMM.2009.06.070402
16	王颖喜, 卢光跃. 基于最大最小特征值之差的频谱感知技术研究[J]. 电子与信息学报, 2010, 32 (11): 2571- 2575.
	WANG Y X , LU G Y . DMM based spectrum sensing method for cognitive radio systems[J]. Journal of Electronics & Information Technology, 2010, 32 (11): 2571- 2575.
17	许炜阳, 李有均, 徐宏乾, 等. 基于随机矩阵非渐近谱理论的协作频谱感知算法研究[J]. 电子与信息学报, 2018, 40 (1): 123- 129.
	XU W Y , LI Y J , XU H Q , et al. Study on cooperative spectrum sensing algorithm based on random matrix non-asymptotic spectral theory[J]. Journal of Electronics & Information Technology, 2018, 40 (1): 123- 129.
18	赵知劲, 胡伟康. 改进的最大最小特征值之差的频谱感知算法[J]. 电子技术应用, 2014, 40 (8): 119-121, 125.
	ZHAO Z J , HU W K . Improved spectrum sensing algorithm based on DMM[J]. Application of Electronic Technique, 2014, 40 (8): 119-121, 125.
19	徐家品, 杨智. 基于随机矩阵特征值比的频谱感知改进算法[J]. 电波科学学报, 2015, 30 (2): 282- 288.
	XU J P , YANG Z . Improved spectrum sensing algorithms based on eigenvalue ratio of random matrix[J]. Chinese Journal of Radio Science, 2015, 30 (2): 282- 288.
20	陈亚. 基于MWC的数字接收机结构以及信号检测方法的研究[D]. 哈尔滨: 哈尔滨工程大学, 2016.
	CHEN Y. Digital receiver structure and the research of signal detection method based on MWC[D]. Harbin: Harbin Engineering University, 2016.
21	ZHANG C J, LI S S, DENG Z A, et al. An improved eigenvalue-based channelized sub-band spectrum detection method[C]//Proc. of the Advanced Hybrid Information Processing, 2019: 244-251.
22	LIU C , WANG J , LIU X M , et al. Maximum eigenvalue-based goodness-of-fit detection for spectrum sensing in cognitive radio[J]. IEEE Trans.on Vehicular Technology, 2019, 68 (8): 7747- 7760. doi: 10.1109/TVT.2019.2923648
23	胡君朋, 古兆兵, 齐卓砾, 等. 基于最小描述长度准则的数字信道化接收机频谱检测方法[J]. 测控技术, 2020, 39 (9): 15- 19.
	HU J P , GU Z B , QI Z L , et al. Spectrum sensing method used in digital channelized receiver based on minimum description length criterion[J]. Measurement & Control Technology, 2020, 39 (9): 15- 19.
24	GIRI M K , MAJUMDER S . On eigenvalue-based cooperative spectrum sensing using feature extraction and maximum entropy fuzzy clustering[J]. Journal of Ambient Intelligence and Humanized Computing, 2023, 14 (8): 10053- 10067. doi: 10.1007/s12652-021-03670-3
25	刘小蒙. 软件无线电中动态信道化技术研究与实现[D]. 郑州: 解放军信息工程大学, 2016.
	LIU X M. Research and implementation of dynamic channelization in software radio[D]. Zhengzhou: Information Engineering University, 2016.
26	周振宇. 动态数字信道化接收机及其子带频谱检测技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2020.
	ZHOU Z Y. Research on dynamic digital channelization recei-ver and its subband spectrum detection technology[D]. Harbin: Harbin Engineering University, 2020.
27	RANINEN E , OLLILA E . Bias adjusted sign covariance matrix[J]. IEEE Signal Processing Letters, 2022, 29, 339- 343. doi: 10.1109/LSP.2021.3134940
28	TULINO A M , VERDU S . Random matrix theory and wireless communications[J]. Foundations and Trends in Communications and Information Theory, 2004, 1 (1): 3- 73.
29	JOHNSTONE I M . On the distribution of the largest eigenva-lue in principal components analysis[J]. The Annals of Statistics, 2001, 29 (2): 295- 327.
30	TRACY C A , WIDOM H . On orthogonal and symplectic matrix ensembles[J]. Communications in Mathematical Physics, 1996, 177 (3): 727- 754. doi: 10.1007/BF02099545

t	F₁(t)	t	F₁(t)
-3.895	0.01	0.450	0.9
-2.782	0.1	0.980	0.95
-1.268	0.5	2.022	0.99