Radar Forward-Looking Super-Resolution Imaging Algorithm of ITR-DTV Based on Renyi Entropy

Abstract

Radar forward-looking super-resolution imaging is a hot spot in the field of radar imaging research. Restricted by Doppler bandwidth and platform size, traditional high-resolution synthetic aperture imaging and real aperture imaging are not suitable for forward-looking imaging, so a deconvolution-based radar forward-looking super-resolution imaging technology is proposed. The traditional methods currently used in the field of forward-looking deconvolution super-resolution imaging of scanning radar have poor ability to recover the texture details of the target image direction, but simply describe the errors of all measurement data uniformly, which leads to an increase in the result error and have poor ability to adapt to different scenarios. So, this article proposes an improved Tikhonov regularization direction total variation (DTV) deconvolution super-resolution algorithm based on Rayleigh entropy. The algorithm introduces the DTV operator to more accurately restore the edge texture details of the image, and adds a weight matrix to the loss function to more accurately reflect the error degree of each measurement value in the loss function. The entropy enhances the applicability of the algorithm in different scenarios, and significantly improves the radar's ability to recover targets in a low signal-to-noise ratio environment. Finally, the simulation data and measured data processing results show that compared with the traditional method in the field of scanning radar forward looking deconvolution super-resolution imaging, the algorithm proposed in this article is better.

Keywords

• Direction total variation (DTV)
• forward-looking radar
• Renyi entropy
• super-resolution