首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >A NOVEL FORWARD-BACKWARD SMOOTHING-BASED LEARNING SUBSPACE METHOD FOR RECOGNITION OF RADAR TARGETS
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A NOVEL FORWARD-BACKWARD SMOOTHING-BASED LEARNING SUBSPACE METHOD FOR RECOGNITION OF RADAR TARGETS

机译:基于新的基于前向后平滑的学习子空间的雷达目标识别

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This paper proposes a novel Forward-Backward Smoothing-Based Learning Subspace Method (FBSLSM), which can satisfy the requirements of being insensitive to the order of presentaton of the training samples, and is of faster convergence speed. This method is applied to the recognition of simulating High Resolution Radar (HRR) targets (two for ships, one for chaff). Moreover, for recognition of HRR targets, a new selection method of subspace dimensionality is given. The computer simulating experiments show that the corresponding performance of proposed FBSLSM such as rate of correct recognition and convergence speed is better than that of the ALSM presented by Oja.
机译:提出了一种新颖的基于前向后平滑的学习子空间方法(FBSLSM),该方法可以满足对训练样本表示顺序不敏感的要求,并且收敛速度更快。该方法适用于模拟高分辨率雷达(HRR)目标的识别(两个用于舰船,一个用于谷壳)。此外,为识别HRR目标,给出了一种新的子空间维数选择方法。计算机仿真实验表明,所提出的FBSLSM的正确识别率和收敛速度等性能优于Oja提出的ALSM。

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