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Open AccessArticle

Optimization of Weighting Window Functions for SAR Imaging via QCQP Approach

by Jin Liu 1,2,3,*, Wei Wang 1,2 and Hongjun Song 1,2
1
Department of Space Microwave Remote Sensing System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
2
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100039, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(2), 419; https://doi.org/10.3390/s20020419
Received: 30 November 2019 / Revised: 3 January 2020 / Accepted: 7 January 2020 / Published: 11 January 2020
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Simulation and Processing)
Weighting window functions are commonly used in Synthetic Aperture Radar (SAR) imaging to suppress the high Peak SideLobe Ratio (PSLR) at the price of probable Signal-to-Noise Ratio (SNR) loss and mainlobe widening. In this paper, based on the method of designing a mismatched filter, we have proposed a Quadratically Constrained Quadratic Program (QCQP) approach, which is a convex that can be solved efficiently, to optimize the weighting window function with both amplitude and phase, expecting to offer better imaging performance, especially on PSLR, SNR loss, and mainlobe width. According to this approach and its modified form, we are able to design window functions to optimize the PSLR or the SNR loss under different kinds of flexible and practical constraints. Compared to the ordinary real-valued and symmetric window functions, like the Taylor window, the designed window functions are complex-valued and can be asymmetric. By using Synthetic Aperture Radar (SAR) point target imaging simulation, we show that the optimized weighting window function can clearly show the weak target hidden in the sidelobes of the strong target. View Full-Text
Keywords: window function; convex optimization; peak sidelobe ratio; signal-to-noise ratio (SNR) loss; synthetic aperture radar (SAR) imaging window function; convex optimization; peak sidelobe ratio; signal-to-noise ratio (SNR) loss; synthetic aperture radar (SAR) imaging
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Liu, J.; Wang, W.; Song, H. Optimization of Weighting Window Functions for SAR Imaging via QCQP Approach. Sensors 2020, 20, 419.

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