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<p>The design of a synthetic aperture radar (SAR) extended scene echo signal simulator that can achieve a desired performance with an optimal buffer utilization is surveyed in this study. We propose an innovative scheme, where the buffer selection is formulated as an optimization problem. The buffer assigning weights are optimized to achieve a predetermined imaging energy. This optimization enables reducing the complexity and cost of the architecture and consequently provides an alternative to the commonly used method for echo component generation. In fact, the final imaging energy can be associated to the number of storing resources with an appropriate <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11760_2024_3793_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">\({l}_{0}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>l</mi> <mn>0</mn> </msub> </math></EquationSource> </InlineEquation> norm constraint. However, the resulting problem is non-convex and requires an NP-hard solution. We put forward two heuristic solutions to tackle the above mentioned problem using greedy selection algorithms. As a result, the optimal solution is achieved with a reasonable computational time with a controllable performance in terms of imaging energy compared to the conventional method.</p>

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Optimal buffer subset selection in extended SAR scene simulator

  • 期刊论文

  • 发表日期：

    2025年1月28日

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  • Sima Shariatmadari,
  • Seyed Mehdi Hosseini Andargoli

摘要

The design of a synthetic aperture radar (SAR) extended scene echo signal simulator that can achieve a desired performance with an optimal buffer utilization is surveyed in this study. We propose an innovative scheme, where the buffer selection is formulated as an optimization problem. The buffer assigning weights are optimized to achieve a predetermined imaging energy. This optimization enables reducing the complexity and cost of the architecture and consequently provides an alternative to the commonly used method for echo component generation. In fact, the final imaging energy can be associated to the number of storing resources with an appropriate \({l}_{0}\) l 0 norm constraint. However, the resulting problem is non-convex and requires an NP-hard solution. We put forward two heuristic solutions to tackle the above mentioned problem using greedy selection algorithms. As a result, the optimal solution is achieved with a reasonable computational time with a controllable performance in terms of imaging energy compared to the conventional method.