Recognition algorithms for small moving target in strong noise based on single feature has a high false alarm. Sometimes the features of target and noise are very alike.
利用单个特征识别强噪声中的弱小运动目标，常因所提取的目标特征与噪声特征易混淆而导致高的虚警率。
The fast recognition algorithms of gasoline brands based on the near-infrared (NIR) spectroscopy is presented. It includes pretreatment, feature extraction and classification modeling.
提出基于近红外（NIR）光谱的汽油牌号快速识别算法，主要包括预处理、特征提取和分类建模几部分，比较了各种分类方法的识别能力。
Recognition algorithms for small moving target in strong noise based on single feature has a high false alarm.
利用单个特征识别强噪声中的弱小运动目标，常因所提取的目标特征与噪声特征易混淆而导致高的虚警率。
Then, the performance of two SVD algorithms and feature subspace radar target recognition algorithm based on SVD are evaluated according to real data of planes.
进而将两种SVD算法应用在特征子空间雷达目标一维距离像识别法中，使用实测数据对其进行速度验证和性能评估。
Then, the performance of two SVD algorithms and feature subspace radar target recognition algorithm based on SVD are evaluated according to real data of planes.
进而将两种SVD算法应用在特征子空间雷达目标一维距离像识别法中，使用实测数据对其进行速度验证和性能评估。
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