基于滑窗和原子字典的压缩域跳频信号参数估计算法

现有跳频信号参数估计算法大多没有考虑跳频信号的结构特性，在低信噪比下存在计算复杂度高或估计精度低的缺点，针对这一问题，该文提出一种基于滑窗和原子字典的压缩域跳频信号参数估计算法。用滑窗法对所处理的跳频信号进行整周期滑动压缩采样，粗略估计出跳频信号的跳变时刻，以块对角化的傅里叶正交基作为稀疏基精确估计出跳变前后的频率，在此基础上构建可以表示跳频信号局部时频特性的原子字典，通过匹配追踪算法准确估计出跳频信号的跳变时刻。实验结果表明，该算法在显著降低信号采样数据量和计算复杂度的同时，保持了跳频信号参数的高精度估计。

Parameter Estimation Algorithm for Frequency-hopping Signal in Compressed Domain Based on Sliding Window and Atomic Dictionary

Most existing parameter estimation algorithms for Frequency Hopping (FH) signal do not consider the structural characteristics of FH signals, and have the disadvantages of high computational complexity or low estimation accuracy in low signal-to-noise ratio circumstance. To solve this problem, this paper proposes a parameter estimation algorithm for frequency hopping signal in compressed domain based on sliding window and atomic dictionary. The frequency hopping signal is acquired by sliding compression sampling, and hopping time is roughly estimated with sliding window method. The Fourier orthogonal basis of block diagonalization is used as sparse basis to estimate the frequency of the signal. An atomic dictionary, which can represent the local time-frequency characteristics of the frequency hopping signal, is constructed based on the estimated frequency and rough hopping time. Then the hopping time can be estimated accurately by the matching pursuit algorithm. Simulation results show that this algorithm can significantly reduce the sampling data and computational complexity, while maintaining the high accuracy estimation.