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中文摘要：
通过分析高分辨距离像（HRRP）的统计特性，提出一种Gamma模型与基于累积量的随机学习算法（SLC）相结合，估计HRRP概率密度的新方法：Gamma-SLC方法。该方法具有Gamma分布针对性强，估计准确与SLC适应性强的优点，同时回避了二者的缺点。另外，借鉴最大熵原则的非高斯性测度，设计了一个新的评价概率密度估计效果的准则。基于外场实测数据的实验证明了Gamma-SLC方法的有效性。
英文摘要：
A novel HRRP probability density estimate method, namely Gamma-SLC, is presented by combining Gamma model and stochastic learning of the cumulative （SLC）. The presented method has the advantages of high pertinence and high accuracy from Gamma distribution and high adaptability from SLC, but avoids the disadvantage of both. In addition, a new criterion for evaluating the estimation of probability density is designed based on maximum-entropy non-Gaussian measurement. Experimental results using outfield real data demonstrate the validity of the presented method.
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