现有的跳频信号处理方法往往需要积累足够长的样本数据, 缺乏实时快速运算的能力, 无法处理高速跳频信号。在小样本条件下提出一种跳频信号实时跟踪和参数估计方法。根据跳频信号的频域稀疏性建立信号模型, 引入稀疏贝叶斯学习 (SBL)算法解决多观测向量 (MMV)信号重构问题。在构建新的判决统计量基础上, 推导一种保持恒虚警概率的跳变时刻检测方法, 设计滑动策略实现跳频信号的实时跟踪。分别利用几何重心法和最小二乘法估计每跳 (hop)的载波频率和来波方向 (DOA)。实验证明, 新方法在低信噪比 (SNR)下具有更低的虚警概率, 参数估计精确度得到明显提升。

The existing Frequency-Hopping(FH) signal processing algorithms often require sufficient data, therefore cannot meet the need of real-time operation or require handling the FH signal with high hopping speed. In order to process FH signals with few samples, a real-time tracking and parameter estimation method is proposed. According to the sparsity in frequency domain, Sparse Bayesian Learning(SBL) is introduced to reconstruct Multiple Measurement Vector(MMV). By constructing new statistic parameter, a hop timing detecting method with constant false alarm probability is derived. Then FH signals can be tracked dynamically according to a sliding strategy. Finally, the proposed method estimates the carrier frequency and Direction-Of-Arrival(DOA) by gravity of geometric center and least square method respectively. Experiments show that the proposed method has lower false alarm probability under low Signal-to-Noise Ratio(SNR), and improves the accuracy of parameter estimation remarkably.