| 摘要: |
| 传统单一特征检测方法的检测性能较差，通过多特征联合检测方法可以有效提高检测性能。采用多特征联合检测方法在提高性能之外，也会造成计算量增加以及信息冗余。对此提出了一种基于线性判别分析的海上目标检测方法，将单一特征映射到二维特征空间中，形成两组特征组合，RDPH?RVE特征组合和RPH?TEM特征组合，并在二维特征组合基础上进行降维处理。通过将单一特征映射到二维空间中，降低海杂波与目标重叠区域，再通过线性判别分析方法，将雷达回波数据在区分性更好的方向进行投影，在保留信息的同时减少了计算量。 |
| 关键词: 特征提取 小目标检测 海杂波 多特征联合 |
| DOI：DOI:10.3969/j.issn.1672-2337.2024.06.011 |
| 分类号:TN951；TN957.51 |
| 基金项目:国家自然科学基金资助项目（No.62388102，62101583） |
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| Detection Algorithm of Maritime Target Based on Linear Discriminant Analysis |
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YAN Wenli， DING Hao， LIU Ningbo， WANG Zhongxun
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1. School of Physics and Electronic Information, Yantai University, Yantai 264005, China;2. Naval Aviation University, Yantai 264001, China
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| Abstract: |
| The detection performance of the traditional single feature method is poor, and the detection performance can be effectively improved by multi?feature joint detection method. However, the use of multi?feature joint methods will not only improve the detection performance, but also lead to an increase in calculation and information redundancy. In this paper, a detection method for floating small targets based on linear discriminant analysis is proposed. The single feature is mapped to a two?dimensional feature space to form two groups of feature combinations, which named RDPH?RVE and RPH?TEM. Dimension reduction is carried out on the basis of two?dimensional feature combination. By mapping a single feature into a two?dimensional space, the overlapping area between sea clutter and the target is reduced. Then through the linear discriminant analysis method, the radar data is projected in a more distinguishable direction, which reduces the amount of calculation while retaining the information. |
| Key words: feature extraction small target detection sea clutter multi⁃feature combination |
