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首页> 外文会议>IEEE International Conference on Acoustics Speech and Signal;ICASSP 2010 >Feature extraction and optimization of representative-slice in ambiguity function for moving radar emitter recognition
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Feature extraction and optimization of representative-slice in ambiguity function for moving radar emitter recognition

机译：用于移动雷达辐射源识别的模糊函数中代表性切片的特征提取与优化

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摘要

Radar emitter recognition is an important and challenging subject in radar signal analysis and processing. In this work, an ambiguity function (AF) representative-slice based feature extraction and optimization algorithm is presented for unintentional modulation recognition of moving radar emitters. It considers near-zero slices of AF as representative feature set of radar emitters, which not only coincides with the characteristics of real radar signals, but also mitigates the computation problem and avoids undesired cross terms in existing AF based method. Direct Discriminant Ratio (DDR) criterion is further utilized to preserve the most discriminant features and boost recognition accuracy, by ranking the kernel points along the representative-slice. Experimental results validate the practical usefulness and high stability of the proposed approach on real data of moving radar emitters, as well as synthetic radar data from U.S. Naval Research Laboratory.
机译：雷达辐射源识别是雷达信号分析和处理中一个重要且具有挑战性的主题。在这项工作中，提出了一种基于模糊度函数（AF）的代表切片的特征提取和优化算法，用于移动雷达辐射源的无意调制识别。它将近零的AF切片视为雷达发射器的代表性特征集，这不仅与真实雷达信号的特征相吻合，而且减轻了计算问题并避免了现有基于AF的方法中不需要的交叉项。通过沿代表切片对内核点进行排序，直接判别比率（DDR）准则进一步用于保留最判别特征并提高识别准确性。实验结果证实了该方法对移动雷达发射器的真实数据以及美国海军研究实验室的合成雷达数据的实用性和高度稳定性。

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