High-Spatial-Resolution Signal Processing for the Airborne Rotating-Antenna Microwave Scatterometers
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摘要: 旋转扫描微波散射计具有观测刈幅宽、系统结构简单、经济的特点,广泛应用于现代海面风场遥感。然而,传统微波散射计一般采用真实孔径雷达工作体制,难以获取高空间分辨率的地表后向散射测量数据。为此,本文以机载平台为例提出了一种基于多普勒波束锐化(Doppler Beam Sharpening, DBS)技术的旋转扫描微波散射计的高空间分辨率信号处理方案,旨在实现高空间分辨率与宽观测刈幅的一体化测量。通过构建涵盖信号收发、脉冲压缩、多普勒锐化等步骤的信号级仿真流程,探究了旋转扫描微波散射计的空间分辨率随扫描方位角变化的特性。结果表明,DBS技术可以使该散射计>74%观测刈幅内的空间分辨率提升至传统方法的10倍以上。然后,利用数值模拟定量分析了信噪比对DBS“成像”质量的影响,为定量化遥感应用提供支撑。本研究虽基于机载平台展开,却为星载旋转扫描微波散射计突破中尺度局限、迈向亚中尺度遥感,提供了关键的算法支撑与验证。Abstract: The rotating-antenna microwave scatterometer is characterized by its wide observation swath, simple system architecture, and cost-effectiveness, thus being widely applied in the remote sensing of sea surface winds. However, conventional microwave scatterometers are generally real-aperture radars, which makes it challenging to obtain high-spatial-resolution measurements of surface backscattering coefficients. To address this limitation, this paper takes an airborne platform as an example, and proposes a high-spatial-resolution signal processing scheme for the rotating-antenna microwave scatterometers, with the aim of achieving both high spatial resolution and wide observation swath based on the Doppler Beam Sharpening (DBS) technique. The variation of spatial resolution with scanning azimuth angle is firstly investigated following a signal-level simulation workflow, which includes signal transmission and reception, pulse compression, Doppler sharpening, etc. The results demonstrate that DBS generally results in a much finer spatial resolution (within 75% of the observation swath) than that of conventional scatterometers. Furthermore, numerical simulations are used to quantify the impact of signal-to-noise ratio (SNR) on the quality of DBS “images”, providing theoretical and technical supports for the quantitative applications of scatterometers. Although this study is based on an airborne platform, it provides crucial algorithmic support and validation for the spaceborne rotating scatterometers to break through mesoscale limitations and move towards sub-mesoscale remote sensing applications.
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