块稀疏贝叶斯模型下的跳频信号时频分析

针对传统时频分析方法存在的时频聚集性差以及交叉项干扰的问题，本文将接收到的跳频信号进行分割，构建时频稀疏模型，利用模型中的统计特性和结构特性采用块稀疏贝叶斯学习算法对跳频信号的时频图进行重构，在不需知道稀疏度和噪声强度的情况下，得到了高精度的时频图。但是由于算法在高维参数空间进行参数估计时复杂度较高，本文采用近似替换的方法对该算法进行改进，将高维参数空间转换到原始参数空间计算，大大减少了算法的复杂度，仿真结果表明改进算法在低信噪比的情况下能有效的得到跳频信号的高精度时频图且复杂度大大降低。 

Time-frequency analysis of frequency-hopping signals based on block sparse Bayesian model

Aiming at the problems of time frequency aggregation and cross term interference in traditional time-frequency analysis methods. The received frequency hopping signals are segmented to constructs a time-frequency sparse model. Then the block sparse Bayesian learning algorithm is used to reconstruct the time-frequency representation of the frequency hopping signals by using the statistical characteristics and structural characteristics of the model. High precision time-frequency representation can be obtained without prior knowledge of sparseness and noise intensity. However, this algorithm has high computational complexity due to the parameter estimation in the high-dimensional parameter space. The approximate substitution method is used in this paper to improve the performance, and the high-dimensional parameter space is converted to the original parameter space. Simulation results show that the improved algorithm can obtain the high precision time-frequency representation of the frequency-hopping signals effectively and reduce the complexity greatly.