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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Detection of Small Floating Targets on the Sea Surface Based on Multi-Features and Principal Component Analysis
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Detection of Small Floating Targets on the Sea Surface Based on Multi-Features and Principal Component Analysis

机译：基于多特征和主成分分析的海面上小浮靶检测

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摘要

This letter proposes a detection method of small floating targets based on multi-features and principal component analysis (PCA) for marine surveillance radar. This method consists of three stages. In the first stage, six features extracted from radar returns are combined into a feature vector. Numerous feature vectors of sea clutter make up a feature matrix. In the second stage, the feature matrix is decomposed into low-rank and sparse components to decrease the influence of sea spikes. In the third stage, a PCA-based anomaly detector with an adjustable false alarm rate is constructed to distinguish the target cells from the clutter-only cells in a feature space because prior information of the target is usually unknown. Experiments using the measured database of an ${X}$ -band radar show that the proposed method attains a state-of-the-art detection rate.
机译：本次信提出了一种基于多特征和主要成分分析（PCA）的小浮动目标的检测方法，用于海洋监测雷达。该方法包括三个阶段。在第一阶段，从雷达返回提取的六个特征被组合成特征向量。海杂波的众多特征向量构成了一个特征矩阵。在第二阶段，特征矩阵被分解成低等级和稀疏组分，以降低海峰值的影响。在第三阶段，构造具有可调节误报率的PCA的异常检测器，以区分特征空间中仅杂乱的小区的目标单元，因为目标的先前信息通常是未知的。使用$ {x} $ -BAND RADAR的测量数据库的实验表明，所提出的方法达到最先进的检测率。

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