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In this paper we propose a general feature partitioning framework to PCA computation and raise issues of cross-sub-pattern correlation, feature ordering dependence, selection of sub-pattern size, overlap of sub-patterns and selection of principal components.
Furthermore, the more various samples of this correlation feature can be obtained for sea clutter.
Then, the statistics results of this correlation feature for the sea clutter and targets are presented in Section 2.3, according to the disparity of the correlation features of clutter and targets, a new correlation feature-based detection scheme is proposed.
The foundation of the works on feedback overhead reduction for MIMO systems is the correlation feature of MIMO channels.
The second approach to the feature selection is based on correlation and called correlation feature selection (CFS) [26].
Therefore, this detector using the correlation feature of sea clutter can effectively eliminate the negative effect of the nonstationary property of sea clutter on the detection performance.
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Thirdly, according to file correlation features, files are classified into three types: structurally-related files, logically-related files, and independent files.
Fig. 5 Flowchart of correlation feature-based detector.
We design a correlation feature-based detector for detecting the range distributed targets embedded within sea clutter.
The main contributions of our present study are summarized as follows: We design a correlation feature-based detector for detecting the range distributed targets embedded within sea clutter.
Inspired by this and the works presented in [34], we design a correlation feature-based detector for detecting the range distributed targets embedded within sea clutter.
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