基于稀疏時頻分布的跳頻信號參數(shù)估計

金艷; 周磊; 姬紅兵

doi:10.11999/JEIT170525

基于稀疏時頻分布的跳頻信號參數(shù)估計

doi: 10.11999/JEIT170525

基金項目:

國家自然科學基金(61201286)，陜西省自然科學基金(2014JM8304)

計量
- 文章訪問數(shù): 1390
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- PDF下載量: 230
- 被引次數(shù): 0
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)時頻分析方法的跳頻信號參數(shù)估計中，采用核函數(shù)抑制時頻分布交叉項會導致時頻聚集性的下降，不利于信號參數(shù)提取。針對此問題，該文提出一種基于稀疏時頻分布(STFD)的跳頻信號處理方法。該方法首先根據(jù)Cohen類分布的原理和跳頻信號模糊函數(shù)的特點，以模糊域矩形窗為核函數(shù)，構建了一種Cohen類的矩形核分布(RKD)。RKD可有效抑制交叉項，但其時頻分辨率較低。為提高RKD的時頻性能，在壓縮感知框架下，利用跳頻信號時頻分布的稀疏特性，對RKD附加稀疏性約束，建立稀疏時頻分布(STFD)的優(yōu)化求解模型。STFD不僅能有效抑制交叉項，而且具有良好的時頻聚集性。仿真分析表明，與傳統(tǒng)時頻分析方法相比，該文提出的基于STFD的跳頻信號參數(shù)估計方法性能更優(yōu)。
- 跳頻信號 /
- 參數(shù)估計 /
- 時頻分布 /
- 稀疏性 /
- 時頻聚集性
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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