采用局部特征尺度分解的跳频信号参数盲估计算法

针对现有跳频信号参数盲估计算法存在时间分辨率和频率分辨率矛盾这一问题，提出了一种基于局部特征尺度分解的跳频信号参数估计新算法。该算法将跳频信号迭代地分解成若干个内禀尺度分量并进行降噪处理，然后对其最大瞬时幅度进行小波变换和傅里叶变换即可估计出跳频信号的跳变时刻和跳频周期，最后根据得到的跳变时刻和跳频周期可以进一步估计出跳频频率集。该算法不受时频不确定性原理的影响，能够在未知先验知识的条件下估计出跳频信号的跳周期、跳变时刻和跳频频率集。最后通过仿真验证了算法的有效性。 

Blind parameter estimation of frequency hopping signal using Local characteristic-scale decomposition

A new blind parameter estimation algorithm of the frequency hopping signal is proposed, which is based on the local characteristic-scale decomposition(LCD) to solve the contradiction between the resolution of time and frequency. In the proposed algorithm the frequency hopping signal is iteratively decomposed into several intrinsic scale components(ISC), and some intrinsic scale components are deleted which are regarded as noise components, then an analysis sequence is derived from the maximum instantaneous amplitude of the denoised signal. So the hop rate and hop timing of the frequency hopping signal can be estimated by performing wavelet transform and Fourier transform on the analysis sequence. And the hopping frequencies can be estimated by using the estimated hop rate and hop timing. The algorithm is free from time-frequency uncertainty principle, and can estimate the hop rate, hop timing and hopping frequencies without any prior knowledge. Simulation results have proved that the algorithm is effective.