Spectral analysis is a significant technique applied in many fields to infer the signal spectral contents. However, the frequency resolution of a signal spectrum estimation result is limited by its finite data length, especially when using a Fourier-based method. Extra processing gain [i.e., signal-to-noise ratio (SNR) improvement] is always required for weak target detection. In this paper, a high-resolution spectrum estimation method using a deconvolution algorithm is proposed. According to classical spectral analysis, a power spectrum derived from a finite data length is related to the convolution of the true power spectrum from an infinite length data set with the power spectrum from a window function. Therefore, using a deconvolution algorithm on the power spectrum estimated by classical spectral analysis can remove the influence from the window function, such as spectral leakage. The deconvolved power spectrum can robustly obtain a sufficiently high-frequency resolution and low sidelobes with relatively few calculations. The proposed method can also provide a deconvolution gain, which plays an important role in weak signal detection as it is capable of enhancing the signal and reducing background noise. Its performance is analyzed in simulations as well as with measured experimental data.

中文翻译：

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使用解卷积的高分辨率功率谱估计方法

频谱分析是一项重要的技术，应用于许多领域以推断信号频谱内容。然而，信号频谱估计结果的频率分辨率受到其有限数据长度的限制，尤其是在使用基于傅立叶的方法时。弱目标检测总是需要额外的处理增益 [即，信噪比 (SNR) 改进]。在本文中，提出了一种使用反卷积算法的高分辨率频谱估计方法。根据经典的频谱分析，从有限数据长度导出的功率谱与来自无限长度数据集的真实功率谱与来自窗函数的功率谱的卷积有关。所以，对经典谱分析估计的功率谱使用去卷积算法可以去除窗函数的影响，例如谱泄漏。去卷积的功率谱可以通过相对较少的计算稳健地获得足够高的频率分辨率和低旁瓣。所提出的方法还可以提供去卷积增益，它在弱信号检测中起着重要作用，因为它能够增强信号并减少背景噪声。其性能在模拟以及测量的实验数据中进行分析。它在弱信号检测中起着重要作用，因为它能够增强信号并降低背景噪声。其性能在模拟以及测量的实验数据中进行分析。它在弱信号检测中起着重要作用，因为它能够增强信号并降低背景噪声。其性能在模拟以及测量的实验数据中进行分析。