mRMR-Tri-ConcaveHull Detector for Floating Small Targets in Sea Clutter

Abstract

For the feature-based detector of small targets in sea clutter, on the one hand, the three-dimensional convex hull-based detector deviates from the distribution of sea clutter vectors in the feature space and only combines the information of low-dimensional features. On the other hand, the redundancy and correlation between high-dimensional features are high. Consequently, we propose a detector for detecting small floating targets in sea clutter in high-dimensional feature space (HDFS) in this article. First, the maximum relevance-minimum redundancy (mRMR) algorithm to choose the low-relatedness features from the HDFS is proposed. For the mRMR algorithm, we choose the target features and sea clutter features from the eight-dimensional feature space (8-DFS), where the target features and sea clutter features have the highest degree of discrimination in the 3-DFS. Second, the distribution of sea clutter in the 3-DFS is concave or convex, which depends on the selection of features. In most cases, the distribution is concave. Using the traditional convex hull to match the concave distribution of sea clutter inevitably enlarges the judgment area and considerably decreases the detection probability. Due to the concave distribution of sea clutter in the 3-DFS, we propose a new false alarm controllable three-dimensional concave hull detector based on the mRMR (mRMR-Tri-ConcaveHull detector). In the mRMR-Tri-ConcaveHull detector, the feature vectors in the 3-DFS, which are selected by mRMR, form a concave area that is more suitable for the Concave Hull detector. Through the experimental analysis of the measured data, we find that the proposed mRMR-Tri-ConcaveHull in this article can significantly enhance the detection performance compared with the three-feature convex hull detector.

Keywords

• Concave hull detector
• maximum relevance-minimum redundancy (mRMR)
• radar
• sea clutter
• target detection