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Profile detection for fiber spectrum data with low SNR based on wavelet and filtering

机译:基于小波和滤波的低信噪比光纤光谱数据轮廓检测

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In this paper, a novel detection method is proposed for fiber spectrum profile signals with low signal-to-noise ratio (SNR). This method can be applied to the processing of astronomical fiber spectrum data. First, profile signal of the target is decomposed into several sub-signals by stationary wavelet transform (SWT) algorithm. For sub-signals, adaptive filters are designed with their respective cutoff frequencies by using the corresponding reference signal (flat signal) in order to separate noise from target signal for getting more accurate information of the signal. Inverse stationary wavelet transform (ISWT) is used to reconstruct the detection signal. At the end of this paper, experiments for different full width at half-maximum (FWHM) of point-spread functions (PSF) are presented to demonstrate the effectiveness of the proposed method.
机译:本文针对低信噪比的光纤光谱信号提出了一种新的检测方法。该方法可以应用于天文纤维光谱数据的处理。首先,通过平稳小波变换(SWT)算法将目标的轮廓信号分解为几个子信号。对于子信号,自适应滤波器通过使用相应的参考信号(平面信号)来设计其各自的截止频率,以便从目标信号中分离出噪声,从而获得更准确的信号信息。逆平稳小波变换(ISWT)用于重建检测信号。在本文的最后,针对点扩展函数(PSF)的不同的半峰全宽(FWHM)进行了实验,以证明该方法的有效性。

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