改进时频脊线的跳频参数盲估计算法

为解决基于时频脊线的跳频参数估计算法在信噪比低于-5 dB时估计误差较大且存在定频干扰时方法失效的问题,提出了该算法的改进算法。在短时傅里叶变换(short time Fourier transform, STFT)的基础上,利用迭代去噪法对原时频图进行去噪处理,根据跳频信号与定频干扰驻留时间的不同,采用k-means算法对其进行聚类,消除定频干扰并提取其时频脊线,利用Haar小波对提取到的时频脊线进行奇异点检测,并估计出跳频信号的跳频周期、起跳时间和跳频频率。仿真结果表明,所提算法在信噪比低于-5 dB且存在较强定频干扰的情况下,仍能对跳频参数进行正确估计,且优于原有算法。

Blind estimation algorithm for frequency hopping parameters of improved time-frequency ridge

In order to solve the problem that the time-frequency ridge-based frequency hopping parameter estimation algorithm has large estimation error when the signal-to-noise ratio (SNR) is lower than -5 dB, and the method fails in the case of fixed-frequency interference, an improved algorithm is proposed. On the basis of short-time Fourier transform (STFT), the original time-frequency diagram is denoised by the iterative method. According to the difference of dwell time between the frequency hopping signal and the fixed-frequency interference, the k-means clustering algorithm is used to eliminate the fixed-frequency interference,and extract its time-frequency ridge. Then the singular point of the extracted time-frequency ridge is detected by Haar wavelet, and the frequency hopping period, start time and hopping frequency are estimated. The simulation results show that the proposed algorithm can accurately estimate the frequency hopping parameters under the condition that the SNR is lower than -5 dB and there is strong fixed-frequency interference, and the estimated results are better than the original algorithm.