摘要
以往的跳频信号参数盲估计方法大多难以适应多个信号同时存在的情况,且需要积累一定数量的样本以后才能从中提取所需要的信息。为了稳定实时地跟踪跳频信号的频率,该文提出一种利用贝叶斯稀疏学习的单/多通道跳频信号频率估计和跳变时刻检测方法来实现多跳频信号频率的实时跟踪。首先建立了多跳频信号的稀疏表示模型,然后介绍了多观测贝叶斯稀疏学习算法及跳变时刻实时检测方法,最后仿真结果验证方法的有效性。
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.
出处
《电子与信息学报》
EI
CSCD
北大核心
2013年第6期1395-1399,共5页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61072120)
新世纪优秀人才支持计划资助课题
关键词
信号处理
跳频
频率估计
跳变时刻
贝叶斯稀疏学习
Signal processing
Frequency Hopping （FH）
Frequency estimation
Hop timing
Sparse Bayesian Learning （SBL）
