分析雷达图像中地物杂波的时空特性，提升目标检测精度。
Modeling and Analysis of Spatial and Temporal Land Clutter Statistics in SAR Imaging Based on MSTAR Data
- 用威布尔和瑞利分布分别拟合杂波的时变与空间特征。
- 基于MSTAR数据集验证，威布尔分布更适合角度变化下的时序分析。
- 为恒虚警率检测算法提供统计基础，适合雷达信号处理研究者。
合成孔径雷达（SAR）成像中地物杂波的统计分析日益重要，是设计鲁棒目标检测算法的关键。提取目标能量需掌握背景杂波的统计特性。本文研究了杂波的空间与时间特性，由于每幅图像采集角度不同，时间分析包含视角变化的影响。采用威布尔、对数正态、伽马和瑞利分布进行建模，以Kullback-Leibler散度为拟合优度评估指标。结果表明，威布尔分布更准确描述随角度变化的时序特性，而瑞利分布对空间杂波建模更优。基于上述分析，结合恒虚警率（CFAR）算法在X波段聚束模式的MSTAR数据集上实现目标检测，实验验证了分析的有效性。
原文摘要 · Abstract (English)
The statistical analysis of land clutter for Synthetic Aperture Radar (SAR) imaging has become an increasingly important subject for research and investigation. It is also absolutely necessary for designing robust algorithms capable of performing the task of target detection in the background clutter. Any attempt to extract the energy of the desired targets from the land clutter requires complete knowledge of the statistical properties of the background clutter. In this paper, the spatial as well as the temporal characteristics of the land clutter are studied. Since the data for each image has been collected based on a different aspect angle; therefore, the temporal analysis contains variation in the aspect angle. Consequently, the temporal analysis includes the characteristics of the radar cross section with respect to the aspect angle based on which the data has been collected. In order to perform the statistical analysis, several well-known and relevant distributions, namely, Weibull, Log-normal, Gamma, and Rayleigh are considered as prime candidates to model the land clutter. The goodness-of-fit test is based on the Kullback-Leibler (KL) Divergence metric. The detailed analysis presented in this paper demonstrates that the Weibull distribution is a more accurate fit for the temporal-aspect-angle statistical analysis while the Rayleigh distribution models the spatial characteristics of the background clutter with higher accuracy. Finally, based on the aforementioned statistical analyses and by utilizing the Constant False Alarm Rate (CFAR) algorithm, we perform target detection in land clutter. The overall verification of the analysis is performed by exploiting the Moving and Stationary Target Acquisition and Recognition (MSTAR) data-set, which has been collected in spotlight mode at X-band, and the results are presented.
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