在海杂波中进行目标检测及其在空间borne SAR成像中的应用
Target Detection in Sea Clutter with Application to Spaceborne SAR Imaging
AI总结：
本文提出利用Weibull分布和CFAR算法改进海杂波中目标检测的方法，通过实验验证其有效性。
摘要：
本文针对海杂波中的目标检测这一具有挑战性的任务进行了研究。我们分析了从场景中接收到的信号的统计特性，并据此根据几种已知的概率分布函数建模从背景海杂波反射的信号幅度。接下来，通过利用Kullback-Leibler（KL）发散度度量作为拟合优度检验，我们将证明在所提出的概率分布中，Weibull分布能够更准确地建模背景海杂波的统计特性。随后，我们利用上述信息设计基于常假警报率（CFAR）算法的自适应阈值，以检测被海杂波掩盖的目标能量。对从加拿大RADARSAT-1卫星收集的实验数据的彻底分析证明了所提出方法的整体有效性。
英文摘要：
In this paper, the challenging task of target detection in sea clutter is addressed. We analyze the statistical properties of the signals which have been received from the scene and based on that, we model the amplitude of the signals that have been reflected from the background sea clutter according to several well-known probability distribution functions. Next, by exploiting the Kullback-Leibler (KL) divergence metric as a goodness-of-fit test, we will demonstrate that among the proposed probability distributions, the Weibull distribution can model the statistical properties of the background sea clutter with higher accuracy. Subsequently, we utilize the aforementioned information to design an adaptive threshold based on the Constant False Alarm Rate (CFAR) algorithm to detect the energy of the targets which have been buried in the sea clutter. Thorough analysis of the experimental data gathered from the Canadian RADARSAT-1 satellite demonstrates the overall effectiveness of the proposed method.
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