雷达距离表连续申报融合方法比较

T. Bieker
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引用次数: 4

摘要

高距离分辨率剖面分类是一种可行的非合作目标识别方法。为了提高分类结果的可靠性和鲁棒性,可以采用决策级身份融合的方法。不同的方法已被用于连续记录的雷达距离廓线声明的累积融合。除了贝叶斯融合等概率技术外,基于Dempster-Shafer或投票算法的非概率方法也成为关注的焦点。在本文中,这些不同的方法比较了飞机识别场景中可能出现的典型情况
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of Fusion Methods for Successive Declarations of Radar Range Pro les
Classification of high-range-resolution profiles is a viable method of non-cooperative target identification. In order to increase the reliability and robustness of the classification result, methods of decision-level identity fusion can be applied. Different approaches have been used for a cumulative fusion of declarations of successively recorded radar range profiles. Besides probabilistic techniques such as the Bayesian fusion, non-probabilistic methods based on Dempster-Shafer or voting algorithms have come into focus. In this paper these different approaches are compared for typical situations which can arise in aircraft identification scenarios
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