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战场损伤评估；改进的KL散度稀疏自动编码机；结构优化；特征选择 1

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基于改进Kullback-Leibler散度稀疏自动编码机的战损评估 Article

Zong-feng QI, Qiao-qiao LIU, Jun WANG, Jian-xun LI

《信息与电子工程前沿（英文）》 2017年第18卷第12期页码 1991-2000 doi: 10.1631/FITEE.1601395

摘要：为解决深度学习网络中隐藏层节点数难以确定的问题，文中提出一种改进的KL（Kullback-Leibler）散度稀疏自动编码机，并将该方法应用到战斗损伤评估中。

关键词：战场损伤评估；改进的KL散度稀疏自动编码机；结构优化；特征选择

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Resampling methods for particle filtering: identical distribution, a new method, and comparable study

Tian-cheng LI,Gabriel VILLARRUBIA,Shu-dong SUN,Juan M. CORCHADO,Javier BAJO

《信息与电子工程前沿（英文）》 2015年第16卷第11期页码 969-984 doi: 10.1631/FITEE.1500199

摘要： Resampling is a critical procedure that is of both theoretical and practical significance for efficient implementation of the particle filter. To gain an insight of the resampling process and the filter, this paper contributes in three further respects as a sequel to the tutorial (Li et al., 2015). First, identical distribution (ID) is established as a general principle for the resampling design, which requires the distribution of particles before and after resampling to be statistically identical. Three consistent metrics including the (symmetrical) Kullback-Leibler divergence, Kolmogorov-Smirnov statistic, and the sampling variance are introduced for assessment of the ID attribute of resampling, and a corresponding, qualitative ID analysis of representative resampling methods is given. Second, a novel resampling scheme that obtains the optimal ID attribute in the sense of minimum sampling variance is proposed. Third, more than a dozen typical resampling methods are compared via simulations in terms of sample size variation, sampling variance, computing speed, and estimation accuracy. These form a more comprehensive understanding of the algorithm, providing solid guidelines for either selection of existing resampling methods or new implementations.

关键词： Particle filter Resampling Kullback-Leibler divergence Kolmogorov-Smirnov statistic