To overcome the disadvantages of a non-cooperative frequency hopping communication system, such as a high sampling rate and inadequate prior information, parameter estimation based on Blind Compressed Sensing (BCS) is proposed. The signal is precisely reconstructed by the alternating iteration of sparse coding and basis updating, and the hopping frequencies are directly estimated based on the results. Compared with conventional compressive sensing, blind compressed sensing does not require prior information of the frequency hopping signals; hence, it offers an effective solution to the inadequate prior information problem. In the proposed method, the signal is first modeled and then reconstructed by Orthonormal Block Diagonal Blind Compressed Sensing (OBD-BCS), and the hopping frequencies and hop period are finally estimated. The simulation results suggest that the proposed method can reconstruct and estimate the parameters of non-cooperative frequency hopping signals with a low signal-to-noise ratio.
Wang L, Zhang B, and Zhao Y. Compressive sampling and rapid reconstruction of broadband frequency hopping signals with interference[J]. Circuits, Systems, and Signal Processing, 2015, 34(5):1535-1547.
[2]
Barbarossa S and Scaglione A. Parameter estimation of spread spectrum frequency-hopping signals using time-frequency distributions[C]. First IEEE Signal Processing Working on Signal Processing Advances in Wireless Communications, Paris, 1997:213-216.
[3]
赵俊, 张朝阳, 赖利峰, 等. 一种基于时频分析的跳频信号参数盲估计方法[J]. 电路与系统学报, 2003, 8(3):46-50. Zhao Jun, Zhang Chao-yang, Lai Li-feng, et al.. Blind parameter estimation of frequency-hopping signals based on time-frequency analysis[J]. Journal of Circuits and Systems, 2003, 8(3):46-50.
[4]
Lv J F, Han Y, and Liang X P. The parameter estimation of non-cooperative frequency hopping signals based on the algorithm of RSPWVD[J]. Applied Mechanics and Materials, 2014, 556/562:4779-4783.
[5]
郭建涛, 王宏远, 余本海. 基于粒子群算法的跳频信号参数估计[J]. 计算机应用研究, 2010, 27(2):512-514. Guo Jian-tao, Wang Hong-yuan, and Yu Ben-hai. Parameter estimation of frequency hopping signal based on particle swarm optimization[J]. Application Research of Computers, 2010, 27(2):512-514.
[6]
Li B, Li Y, and Zhu Y. Compressive frequency estimation for frequency hopping signal[C]. TENCON 2013-2013 IEEE Region 10 Conference (31194), 2013:1-4.
[7]
吕晨杰, 王斌, 唐涛. 采用局部特征尺度分解的跳频信号参数盲估计算法[J]. 信号处理, 2015, 31(3):308-313. Lv Chen-jie, Wang Bin, and Tang Tao. Blind parameter estimation of frequency hopping signal using local characteristic-scale decomposition[J]. Journal of Signal Processing, 2015, 31(3):308-313.
[8]
Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.
[9]
Candès E J, Romberg J, and Tao T. Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2):489-509.
[10]
Candè E J and Wakin M B. An introduction to compressive sampling[J]. IEEE Magazine Signal Processing, 2008, 25(2):21-30.
[11]
朱莹, 张弓, 张劲东. 基于DCS的统计MIMO雷达信号模型及参数估计[J]. 雷达学报, 2012, 1(2):143-148. Zhu Ying, Zhang Gong, and Zhang Jin-dong. Signal model and parameters estimation of statistical MIMO radar based on distributed compressed sensing[J]. Journal of Radars, 2012, 1(2):143-148.
[12]
Gleichman S and Eldar Y C. Blind compressed sensing[J]. IEEE Transactions on Information Theory, 2011, 57(10):6958-6975.
[13]
Kreutz-Delgado K, Murray J F, Rao B D, et al.. Dictionary learning algorithms for sparse representation[J]. Neural Computation, 2003, 15(2):349-396.
[14]
Fang B, Huang G, and Gao J. Sub-nyquist sampling and reconstruction model of LFM signals based on blind compressed sensing in FRFT domain[J]. Circuits, Systems, and Signal Processing, 2015, 34(2):419-439.
[15]
方标, 黄高明, 高俊. LFM宽带雷达信号的盲压缩感知模型[J]. 航空学报, 2014, 35(8):2261-2270. Fang Biao, Huang Gao-ming, and Gao Jun. A blind compressed sensing, model for linear frequency modulated wideband radar signals[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(8):2261-2270.
2016, Vol. 5 
