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 that doppler parameters vary with the skew and the image resolution is low 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 by using FRFT instead of matched filtering. In order 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 momentum method is used to perform iterative calculation. Finally, a higher resolution SAR image is obtained. In order 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 main lobe width of the algorithm is narrower, the sidelobe is lower, and the image is clearer.

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, DOI: 10.7523/j.ucas.2022.084.

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