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<p>To enhance the effectiveness of rescue operations, particularly in the context of fire outbreaks and building collapses, the adoption of through-the-wall radar imaging (TWRI) technology is being explored to provide pre-event situational awareness, thereby increasing the likelihood of survival. The critical nature of time during rescue missions is underscored by the fact that the majority of existing image reconstruction algorithms fail to operate within the essential four-minute survival window for humans. Recent advancements have introduced a limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS)-based algorithm within the TWRI framework, which has demonstrated a significant reduction in image reconstruction time, positioning it as a viable solution for time-sensitive operations. This study rigorously evaluates the performance of the LBFGS algorithm to ascertain its applicability in rescue scenarios. Utilizing MATLAB, the algorithm was systematically assessed across various parameters, including the number of targets, data volumes, room dimensions, and signal-to-noise ratios. In all tested scenarios, the LBFGS-based algorithm consistently reconstructed images within the four-minute threshold, achieving satisfactory mean square error values and outperforming traditional sparse reconstruction algorithms. The findings suggest that the LBFGS algorithm is suitable for urgent rescue missions. </p>

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Performance evaluation of limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm for image reconstruction in through-the-wall radar imaging for rescue missions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月12日

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  • Tumaini Edgar,
  • Abdi T. Abdalla,
  • Abdullah F. Ally

摘要

To enhance the effectiveness of rescue operations, particularly in the context of fire outbreaks and building collapses, the adoption of through-the-wall radar imaging (TWRI) technology is being explored to provide pre-event situational awareness, thereby increasing the likelihood of survival. The critical nature of time during rescue missions is underscored by the fact that the majority of existing image reconstruction algorithms fail to operate within the essential four-minute survival window for humans. Recent advancements have introduced a limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS)-based algorithm within the TWRI framework, which has demonstrated a significant reduction in image reconstruction time, positioning it as a viable solution for time-sensitive operations. This study rigorously evaluates the performance of the LBFGS algorithm to ascertain its applicability in rescue scenarios. Utilizing MATLAB, the algorithm was systematically assessed across various parameters, including the number of targets, data volumes, room dimensions, and signal-to-noise ratios. In all tested scenarios, the LBFGS-based algorithm consistently reconstructed images within the four-minute threshold, achieving satisfactory mean square error values and outperforming traditional sparse reconstruction algorithms. The findings suggest that the LBFGS algorithm is suitable for urgent rescue missions.