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Parameter estimation of non-modulated or modulated Frequency-Hopping signals

机译:非调制或调制跳频信号的参数估计

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It is a very effective way to use time-frequency distribution to analyze the Frequency-Hopping (FH) signals. There are a variety of time-frequency analysis methods, in which wavelet transform's time-frequency distribution of the signal is very sensitive to noise, and Wigner-Ville distribution has a good time-frequency aggregation but the presence of severe crosstalk analysis of multi-component signals. Classic STFT is a good time-frequency tools, but cannot obtain a higher time resolution and frequency resolution at the same time. In this paper, the classical STFT algorithm is improved to work well in lower SNR by using image processing, and further improve time resolution at the same time combined with the differential frequency discrimination in high SNR. Experimental results show that reasonable input parameters will improve the performance of frequency hopping signal parameter estimation.
机译:使用时频分布来分析跳频(FH)信号是一种非常有效的方法。有多种时频分析方法,其中信号的小波变换的时频分布对噪声非常敏感,Wigner-Ville分布具有良好的时频聚集性,但是存在严重的串扰分析分量信号。经典STFT是很好的时频工具,但不能同时获得更高的时间分辨率和频率分辨率。本文对经典的STFT算法进行了改进,使其能够通过图像处理在低信噪比下很好地工作,并结合高信噪比下的差分频率识别,进一步提高了时间分辨率。实验结果表明,合理的输入参数将提高跳频信号参数估计的性能。

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