Abstract:Most of previous blind parameter estimation methods of Frequency Hopping (FH) signals with a single channel do not adapt to overlapped signals. Moreover, the single- and multiple-channel-based methods use batch processing techniques almost, so they are not able to estimate FH signals in real time. To get real-time tracking reliably, a novel method for single- and multiple-channel-based frequency estimating and hop timing detecting for FH signals is proposed to track the frequency of multiple frequency-hopping signals based on Sparse Bayesian Learning (SBL). Firstly, overlapped FH signals sparse representation model is established. Then, the Sparse Bayesian Learning is used to estimate hopping frequencies and detect the frequency hops once they happen. Numerical examples are carried out to demonstrate the effectiveness of the proposed method.
王丰华, 沙志超, 刘章孟, 黄知涛. 基于贝叶斯稀疏学习的多跳频信号频率跟踪方法[J]. 电子与信息学报, 2013, 35(6): 1395-1399.
Wang Feng-Hua, Sha Zhi-Chao, Liu Zhang-Meng, Huang Zhi-Tao. A Frequency Tracking Method for Multiple Frequency-hopping Signals Based on Sparse Bayesian Learning. , 2013, 35(6): 1395-1399.