Abstract
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.
Highlights
In many high-power radar systems like the Synthetic Aperture Radar (SAR) system, the LinearFrequency Modulation (LFM) signal is used, which is known as the chirp signal [1]
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
The existing window functions may not be able to offer the performance with Pareto optimality [8] if we take Peak SideLobe Ratio (PSLR), Signal-to-Noise Ratio (SNR) loss as objectives and mainlobe width as a constraint
Summary
In many high-power radar systems like the Synthetic Aperture Radar (SAR) system, the Linear. In [26], a new performance metric of the sidelobe was proposed, which was to be optimized with the design of the mismatched filter by using the closed-form solution and the iterative method. We reformulate the PSLR optimization problem of the window function as an equivalent QCQP, and this QCQP is convex, meaning it can be solved effectively and efficiently by convex optimization methods, such as interior point methods [28,29]. We compare the results with the Taylor window function output, showing that this approach is an effective, efficient, and flexible optimization method that can provide better imaging performance and accommodate diverse cases, like requirements on PSLR or SNR loss.
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