基于ArcSAR的机场跑道FOD快速成像方法

      ArcSAR-based rapid imaging method for foreign object debris on airport runways

      • 摘要: 实时性要求与成本限制是制约机场跑道异物(foreign object debris, FOD)检测雷达大规模部署的关键因素。为此,本文设计了一套零中频W波段圆弧合成孔径雷达(arc-synthetic aperture radar, ArcSAR)系统,该系统兼具高实时性与低功耗等特性,可在复杂机场环境下快速获取高分辨SAR图像。针对传统后向投影(back projection, BP)、距离-多普勒(range-Doppler, RD )等算法计算复杂度高、内存占用大等问题,本文提出一种改进的RD方法:通过建立信号模型与运动几何模型,解析徙动量与多普勒频域关系,设计相位补偿参数实现精准距离徙动校正 (range cell migration correction, RCMC );并通过分析方位向响应函数的频率特性,利用其频谱支撑域较窄的特点,提出基于能量近似的支撑域估计方法,并引入Chirp-Z变换实现高效方位向积累。实测结果表明,与传统RD成像方法相比,本文所提算法在保证成像质量的情况下,有效降低了算法计算复杂度与内存需求,同时具备更低的成像背景噪声,显著降低了对硬件平台的要求。

         

        Abstract: Real-time performance and cost constraints are critical factors limiting the large-scale deployment of foreign object debris (FOD) detection radars on airport runways. To address this challenge, this study presents a zero-intermediate-frequency (zero-IF) W-band arc-scanning synthetic aperture radar (SAR) system that combines high real-time processing capability with low power consumption, enabling rapid acquisition of high-resolution SAR images in complex airport environments. To meet the stringent requirements of real-time operation and low power consumption, this paper investigates SAR imaging algorithms. Conventional back-projection (BP) and range-Doppler (RD) algorithms suffer from high computational complexity and excessive memory usage. To overcome these limitations, we propose an improved RD method: A joint signal and motion geometry model is established to analytically derive the coupling between range cell migration (RCM) and Doppler frequency, enabling precise phase compensation for RCM correction. Leveraging the narrow spectral support of the azimuth response function, an energy-approximation-based support region estimation method is introduced, followed by Chirp-Z transform (CZT) for efficient azimuth integration. Experimental results demonstrate that compared to traditional RD imaging methods, the proposed algorithm effectively reduces computational complexity and memory requirements while maintaining imaging quality. Additionally, it achieves lower background noise in the imaging results, significantly lowering the demands on hardware platforms.

         

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