基于稀疏時(shí)頻分布的跳頻信號(hào)參數(shù)估計(jì)

金艷; 周磊; 姬紅兵

doi:10.11999/JEIT170525

基于稀疏時(shí)頻分布的跳頻信號(hào)參數(shù)估計(jì)

doi: 10.11999/JEIT170525 cstr: 32379.14.JEIT170525

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61201286)，陜西省自然科學(xué)基金(2014JM8304)

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出版歷程
- 收稿日期: 2017-05-31
- 修回日期: 2017-11-06
- 刊出日期: 2018-03-19

Parameter Estimation of Frequency-hopping Signals Based on Sparse Time-frequency Distribution

Funds:

The National Natural Science Foundation of China (61201286), The Natural Science Foundation of Shaanxi Province (2014JM8304)

摘要

摘要: 基于常規(guī)時(shí)頻分析方法的跳頻信號(hào)參數(shù)估計(jì)中，采用核函數(shù)抑制時(shí)頻分布交叉項(xiàng)會(huì)導(dǎo)致時(shí)頻聚集性的下降，不利于信號(hào)參數(shù)提取。針對(duì)此問(wèn)題，該文提出一種基于稀疏時(shí)頻分布(STFD)的跳頻信號(hào)處理方法。該方法首先根據(jù)Cohen類分布的原理和跳頻信號(hào)模糊函數(shù)的特點(diǎn)，以模糊域矩形窗為核函數(shù)，構(gòu)建了一種Cohen類的矩形核分布(RKD)。RKD可有效抑制交叉項(xiàng)，但其時(shí)頻分辨率較低。為提高RKD的時(shí)頻性能，在壓縮感知框架下，利用跳頻信號(hào)時(shí)頻分布的稀疏特性，對(duì)RKD附加稀疏性約束，建立稀疏時(shí)頻分布(STFD)的優(yōu)化求解模型。STFD不僅能有效抑制交叉項(xiàng)，而且具有良好的時(shí)頻聚集性。仿真分析表明，與傳統(tǒng)時(shí)頻分析方法相比，該文提出的基于STFD的跳頻信號(hào)參數(shù)估計(jì)方法性能更優(yōu)。
- 跳頻信號(hào) /
- 參數(shù)估計(jì) /
- 時(shí)頻分布 /
- 稀疏性 /
- 時(shí)頻聚集性
Abstract: In the case of parameter estimation of Frequency Hopping (FH) signal based on conventional time- frequency analysis, the suppression of cross-terms in Time-Frequency Distribution (TFD) by kernel function always leads to the decrease of time-frequency concentration, which is adverse to signal parameter extraction. To deal with this problem, a kind of Sparse TFD (STFD) based FH signals processing method is proposed. Based on the principle of Cohen's class of TFD and the ambiguity function characteristics of FH signals, a Rectangle-shaped Kernel Distribution (RKD) is constructed by choosing the rectangle function in ambiguity domain as its kernel function. RKD can suppress the cross-terms effectively but is followed by poor time-frequency resolution. In order to improve the performance of RKD, the TFD sparsity of FH signals is analyzed and utilized, and the optimal model of STFD is established by additional constraints to RKD under the Compressed Sensing (CS) frame. STFD can not only restrain cross-terms effectively, but also has a high time-frequency concentration. Simulation results show that proposed STFD based parameter estimation of FH signals has better performance compared with conventional ones.
- Frequency-Hopping (FH) signals /
- Parameter estimation /
- Time-Frequency Distributions (TFD) /
- Sparsity /
- Time-frequency concentration

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參考文獻(xiàn)(27)

LEE J and YOON D. Improved FH acquisition scheme in partial-band noise jamming[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(6): 3070-3076. doi: 10.1109/TAES.2016.160071.

TORRIERI D J. Mobile frequency-hopping CDMA systems[J]. IEEE Transactions on Communications, 2000, 48(8): 1318-1327. doi: 10.1109/26.864169.

LIU F, MARCELLIN M W, GOODMAN N A, et al. Compressive sampling for detection of frequency-hopping spread spectrum signals[J]. IEEE Transactions on Signal Processing, 2016, 64(21): 5513-5524. doi: 10.1109/TSP.2016. 2597122.

陳瑩, 鐘菲, 郭樹(shù)旭. 非合作跳頻信號(hào)參數(shù)的盲壓縮感知估計(jì)[J]. 雷達(dá)學(xué)報(bào), 2016, 5(5): 531-537. doi: 10.12000/JR15106.

CHEN Ying, ZHONG Fei, and GUO Shuxu. Blind compressed sensing parameter estimation of non-cooperative frequency hopping signal[J]. Journal of Radars, 2016, 5(5): 531-537. doi: 10.12000/JR15106.

錢怡, 馬慶力, 路后兵. 基于改進(jìn)SPWVD的DS/FH信號(hào)跳頻參數(shù)估計(jì)方法[J]. 艦船電子對(duì)抗, 2015, 38(1): 50-53. doi: 10.16426/j.cnki.jcdzdk.2015.01.012.

QIAN Yi, MA Qingli, and LU Houbing. Estimation method of frequency hopping parameter of DS/FH signal based on improved SPWVD[J]. Shipboard Electronic Countermeasure, 2015, 38(1): 50-53. doi: 10.16426/j.cnki.jcdzdk.2015.01.012.

雷迎科, 鐘子發(fā), 吳彥華. 基于RSPWVD高速跳頻信號(hào)跳周期估計(jì)算法[J]. 系統(tǒng)工程與電子技術(shù), 2008, 30(5): 803-805. doi: 10.3321/j.issn:1001-506X.2008.05.006.

LEI Yingke, ZHONG Zifa, and WU Yanhua. Hop duration estimation algorithm for high-speed frequency-hopping signals based on RSPWVD[J]. Systems Engineering and Electronics, 2008, 30(5): 803-805. doi: 10.3321/j.issn:1001- 506X.2008.05.006.

金艷, 彭營(yíng), 姬紅兵. 穩(wěn)定分布噪聲中基于最優(yōu)核時(shí)頻分析的跳頻信號(hào)參數(shù)估計(jì)[J]. 系統(tǒng)工程與電子技術(shù), 2015, 37(5): 985-991. doi: 10.3969/j.issn.1001-506X.2015.05.01.

JIN Yan, PENG Ying, and JI Hongbing. Parameter estimation of FH signals based on optimal kernel time- frequency analysis in stable distribution noise[J]. Systems Engineering and Electronics, 2015, 37(5): 985-991. doi: 10.3969/j.issn.1001-506X.2015.05.01.

沙志超, 黃知濤, 周一宇, 等. 基于時(shí)頻稀疏性的跳頻信號(hào)時(shí)頻圖修正方法[J]. 宇航學(xué)報(bào), 2013, 34(6): 848-853. doi: 10.3873/j.issn.1000-1328.2013.06.015.

SHA Zhichao, HUANG Zhitao, ZHOU Yiyu, et al. A modification method for time-frequency pattern of frequency- hopping signals based on timefrequency sparsity[J]. Joumal of Astmnautics, 2013, 34(6): 848-853. doi: 10.3873/j.issn. 1000-1328.2013.06.015.

王磊, 姬紅兵, 史亞. 基于模糊函數(shù)特征優(yōu)化的雷達(dá)輻射源個(gè)體識(shí)別[J]. 紅外與毫米波學(xué)報(bào), 2011, 30(1): 74-79.

WANG Lei, JI Hongbing, and SHI Ya. Feature optimization of ambiguity function for radar emitter recognition[J]. Journal of Infrared and Millimeter Waves, 2011, 30(1): 74-79.

COHEN L. Time-frequency distributions-a review[J]. Proceedings of the IEEE, 1989, 77(7): 941-981. doi: 10.1109/ 5.30749.

BOASHASH B. Time-frequency Signal Analysis and Processing: A Comprehensive Reference[M]. Salt Lake City, UT, USA, American Academic Press, 2015: 151-157.

OBERLIN T, MEIGNEN S, and PERRIER V. Second-order synchrosqueezing transform or invertible reassignment? towards ideal time-frequency representations[J]. IEEE Transactions on Signal Processing, 2015, 63(5): 1335-1344. doi: 10.1109/TSP.2015.2391077.

石光明, 劉丹華, 高大化, 等. 壓縮感知理論及其研究進(jìn)展[J]. 電子學(xué)報(bào), 2009, 37(5): 1070-1081.

SHI Guangming, LIU Danhua, GAO Dahua, et al. Advances in theory and application of Compressed Sensing[J]. Acta Electronica Sinica, 2009, 37(5): 1070-1081.

BARANIUK R G. Compressive sensing[J]. IEEE Signal Processing Magazine, 2007, 24(4): 118-121.

CHEN S S, DONOHO D L, and SAUNDERS M A. Atomic decomposition by basis pursuit[J]. SIAM Review, 2001, 43(1): 129-159.

CANDES E J and TAO T. Near-optimal signal recovery from random projections: universal encoding strategies?[J]. IEEE Transactions on Information Theory, 2006, 52(12): 5406-5425. doi: 10.1109/TIT.2006.885507.

ZHANG Z, XU Y, YANG J, et al. A survey of sparse representation: algorithms and applications[J]. IEEE Access, 2015, 3: 490-530. doi: 10.1109/ACCESS.2015.2430359.

TONG C, LI J, and ZHANG W. Improved RIC bound for the recovery of sparse signals by orthogonal matching pursuit with noise[J]. Electronics Letters, 2016, 52(23): 1956-1958. doi: 10.1049/el.2016.1523.

ZENG J, LIN S, and XU Z. Sparse regularization: convergence of iterative jumping thresholding algorithm[J]. IEEE Transactions on Signal Processing, 2016, 64(19): 5106-5118. doi: 10.1109/TSP.2016.2595499.

FIGUEIREDO M A T, NOWAK R D, and WRIGHT S J. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 586-597. doi: 10.1109/JSTSP.2007.910281.

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