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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Separation of Multicomponent Chirp Signals Using Morphological Component Analysis and Fractional Fourier Transform
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Separation of Multicomponent Chirp Signals Using Morphological Component Analysis and Fractional Fourier Transform

机译：使用形态分析和分数傅里叶变换分离多组分啁啾信号

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摘要

A chirp signal is a large-bandwidth signal which is widely used in engineering. In many applications, it is necessary to decompose a mixed chirp signal into its components. However, the traditional Fourier transform method cannot process a mixed chirp signal when its components intersect in the joint time-frequency domain. Combining the advantages of the morphological component analysis (MCA) with multicomponent signal processing and the fractional Fourier transform (FrFT) in chirp signal processing, this letter proposes the MCA-FrFT method to decompose a multicomponent chirp signal. First, the cost function is defined using the FrFT and optimized by the split augmented Lagrangian shrinkage algorithm (SALSA), and then, all the morphological components are obtained. The proposed method is verified by simulations, and simulation results show that the proposed method has good performance in separating the multicomponent chirp signals into components. Besides, the proposed method is evaluated experimentally in the sea target detection, and the experimental results confirm that the proposed method can not only extract the low observable targets from a heavy sea-clutter environment but also separate them from each other.
机译：啁啾信号是广泛用于工程的大带宽信号。在许多应用中，必须将混合的啁啾信号分解为其组件。然而，当其组件在关节时频域中相交时，传统的傅立叶变换方法无法处理混合啁啾信号。与多组分信号处理的形态分析（MCA）的优点与Chirp信号处理中的分数傅立叶变换（FRFT）相结合，本函数提出了MCA-FRFT方法来分解多组分啁啾信号。首先，使用FRFT定义成本函数，并通过分离增强拉格朗日收缩算法（SALSA）进行优化，然后获得所有形态分量。通过仿真验证所提出的方法，仿真结果表明，该方法在将多组分啁啾信号分离成组件方面具有良好的性能。此外，该方法在实验中进行了在海靶检测中进行评估，实验结果证实，该方法不仅可以从沉重的海洋杂乱环境中提取低可观察目标，还可以彼此分开。

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