基于原子分解的跳频信号盲检测和参数盲估计算法

本文针对跳频信号的检测和参数估计问题，提出了一种基于原子分解算法的跳频信号盲检测和参数盲估计算法。以Gabor函数为基函数对输入的多分量信号样本进行原子分解，在原子分解的每一次迭代后计算信号残差的信息论准则测度，通过与前一次迭代后的信息论准则测度作比较，找到信息论准则测度的第一个局部极小值，这个局部极小值对应的迭代次数就是信号样本中包含的信号分量的数量，进而根据分解得到的时频原子的参数值来聚类，从输入信号中分选出跳频信号的hop，并估计跳频信号的参数。仿真实验表明，该方法能够在未知任何先验知识的情况下，对高斯白噪声环境中跳频信号进行有效检测，并能够对跳周期、跳变时刻和跳频频率进行有效估计。 

Blind detection and parameter estimation algorithm based on atomic decomposition

An algorithm to detect frequency hopping signals and estimate parameters of frequency hopping signals is presented. The proposed algorithm is based on atomic decomposition and information theoretic criterion. The stopping criteria of atomic decomposition is supplied by intercomparing the information theoretic criterion measure of each iteration to find the first local minimum. The number of valid iterations which correlates with the first local minimum equals to the number of signals in the sample. Detection and parameter estimation are performed after atomic decomposition. The proposed algorithm can be performed without any prior knowledge about signals. Numerical experiments have shown the efficiency of the proposed algorithm.