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中文摘要：
针对雷达目标识别中，参数化方法估计高分辨距离像的概率密度存在的“模型失配”问题，提出一种非参数化方法——基于累计量的随机学习算法，估计距离像的概率密度。该算法运用多层感知器估计训练样本的分布函数，然后求导得到概率密度。该算法不仅能全面、精确地估计概率密度，而且回避了许多其他非参数方法面临的“窗宽”敏感性问题。基于外场实测数据的实验证明了该文方法的有效性。
英文摘要：
In order to solve the problem of model mismatch when using parametric approach to estimate the density of High-Resolution Range Profile（HRRP） in radar target recognition, a nonparametric method--Stochastic Learning of the Cumulative（SLC） is presented for the density estimation of HRRP. SLC uses a multiplayer network to estimate the distribution function of the training samples and obtains density by taking derivative. SLC not only describes the density function more comprehensive and accurately, but also avoids the problem of being sensitive to window width that many nonparametric approaches may suffer. Experimental results using outfield real data demonstrate the validity of the proposed learning algorithm.
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同项目期刊论文
Pattern Expression Nonnegative Matrix Factorization: algorithm and Applications to Blind Source Sepa
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