Chirp scaling algorithm based on fractional Fourier transform and image weighted entropy

doi:10.7523/j.ucas.2022.084

Abstract

Abstract: In order to solve the problem of Doppler parameters varying with skew and low image resolution in the traditional chirp scaling (CS) imaging algorithm based on Fourier transform and matched filtering, an algorithm to optimize CS imaging algorithm using fractional Fourier transform (FRFT) is proposed. Firstly, the echo signal model of squint synthetic aperture radar (SAR) is established, and the echo signal model is derived using FRFT instead of matched filtering. To search for the optimal azimuth rotation angle, the cost function of the image is established according to the weighted minimum entropy, and the gradient descent optimization algorithm of the momentum method is used for iterative calculation. Finally, a higher-resolution SAR image is obtained. To verify the effectiveness of the algorithm, experiments were carried out on point target simulation data and measured SAR data sets respectively. The results show that, compared with the traditional CS imaging algorithm, the proposed algorithm achieves a narrower main lobe width, lower sidelobe, and clearer images.

Key words: fractional Fourier transform, chirp scaling imaging algorithm, weighted minimum entropy, squint synthetic aperture radar, matched filtering

CLC Number:

TN958

SHANG Min, XU Xianghui. Chirp scaling algorithm based on fractional Fourier transform and image weighted entropy[J]. Journal of University of Chinese Academy of Sciences, 2024, 41(5): 644-653.

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