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Sparsity of the ISAR images is exploited with the aim to use the possibility of applying an under-sampling strategy as assumed by the compressive sensing approach. The signal sparsity is a desirable property that needs to be satisfied in order to reconstruct the signals and images from the compressive sensed data. It is assumed that certain amount of radar data is not available and the idea is to...
In this paper, we investigate ground moving target imaging (GMTIm) by synthetic aperture radar (SAR) under sparse Bayesian learning (SBL) framework. To automatically determine the parametric dictionary used in the framework, an novel time-frequency representation method, known as Lv's distribution (LVD), is adopted, which is superior to represent multiple moving targets on the Doppler centroid frequency...
This paper presents the quantitative analysis of signal-to-noise ratio (SNR) for the local polynomial Fourier transform (LPFT) used for application of inverse synthetic aperture radar (ISAR) imaging. The relationship between the LPFT and Wigner-Ville distribution (WVD) is derived and theoretical analysis on the SNR achieved by using the LPFT is given. Comparisons on the SNR performances achieved by...
In this paper the local polynomial Fourier transform is introduced. Theoretical analysis and comparisons on the SNR ratio achieved by using the local polynomial Fourier transform, the short time Fourier transform, and the Fourier transform are presented to illustrate the merit of the local polynomial Fourier transform. Improved performance for radar imaging using the local polynomial Fourier transform...
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