论文标题
在失真和干扰下光谱峰的稳健，非参数，有效的分解
Robust, Nonparametric, Efficient Decomposition of Spectral Peaks under Distortion and Interference
论文作者
论文摘要
我们提出了一种在观察到的频谱中的光谱峰的分解方法，该方法通过使用快速傅立叶变换有效地获取。与传统的波形拟合方法相反，我们从更强大的角度优化了该问题。我们将频谱中的峰值建模为伪对称函数，其中唯一的约束是距离增加时围绕中心频率的非进化行为。我们的方法对观察系统可能引起的频谱上的任意失真，干扰和噪声更为强大。我们方法的时间复杂性是线性的，即每个提取的光谱峰$ O（n）$。此外，分解的光谱峰显示出伪正交的行为，它们符合保持平等的能力。
We propose a decomposition method for the spectral peaks in an observed frequency spectrum, which is efficiently acquired by utilizing the Fast Fourier Transform. In contrast to the traditional methods of waveform fitting on the spectrum, we optimize the problem from a more robust perspective. We model the peaks in spectrum as pseudo-symmetric functions, where the only constraint is a nonincreasing behavior around a central frequency when the distance increases. Our approach is more robust against arbitrary distortion, interference and noise on the spectrum that may be caused by an observation system. The time complexity of our method is linear, i.e., $O(N)$ per extracted spectral peak. Moreover, the decomposed spectral peaks show a pseudo-orthogonal behavior, where they conform to a power preserving equality.
