基于稀疏时频分布的跳频信号参数估计

基于常规时频分析方法的跳频信号参数估计中，采用核函数抑制时频分布交叉项会导致时频聚集性的下降，不利于信号参数提取。针对此问题，该文提出一种基于稀疏时频分布(STFD)的跳频信号处理方法。该方法首先根据Cohen类分布的原理和跳频信号模糊函数的特点，以模糊域矩形窗为核函数，构建了一种Cohen类的矩形核分布(RKD)。RKD可有效抑制交叉项，但其时频分辨率较低。为提高RKD的时频性能，在压缩感知框架下，利用跳频信号时频分布的稀疏特性，对RKD附加稀疏性约束，建立稀疏时频分布(STFD)的优化求解模型。STFD不仅能有效抑制交叉项，而且具有良好的时频聚集性。仿真分析表明，与传统时频分析方法相比，该文提出的基于STFD的跳频信号参数估计方法性能更优。

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

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.