用于信号过滤的 DWT 或 WP
我正在处理一个与小波变换相关的棘手问题（至少对我来说很棘手:)。我有一个信号，比如说一个正弦曲线（频率 f1）与另一个正弦曲线（频率 f2）叠加。如果另一个信号的频率高于原始信号的频率，则其过滤不会出现问题。然而，这不是我的情况，因为我必须处理两个具有相似频率的信号，例如，f2 = 1.2 f1。有什么方法可以使用小波变换（最好是 DWT 或小波包）重建原始正弦曲线？我可能会更好地受益于 CWT，因为它显示了完整的时间尺度属性，但这不是选择。
非常感谢。
I'm dealing with a tricky problem related to the wavelet transform (tricky at least for me :). I have a signal, say a sinusoid (frequency f1) with another sinusoid (freq. f2) superposed. If the other signal has higher frequency than the original one, no problem with its filtration appears. However, this is not my case as I have to deal with two signals with similar frequencies, for example, f2 = 1.2 f1. Is there any way to reconstruct the original sinusoid using wavelet transformation, preferably DWT or wavelet packages? I would probably better benefit from CWT as it shows complete time-scale properties, but it is not the option.
Many thanks in advance.
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您正在研究频率与时间不确定性问题。您将需要更长的基向量来分离频率更接近的频谱内容。
对于 0.2 的 delta F，您可能需要尝试使用比感兴趣的正弦曲线周期长 10 倍范围内的基向量。
You are looking at a frequency versus time uncertainty issue. You will need longer basis vectors to separate spectral content that is closer together in frequency.
For a delta F of 0.2, you might want to try using basis vectors that are in the range of 10 times longer than the period(s) of the sinusoids of interest.