| 基于核的k-最近邻在水下目标识别中的应用* |
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| 引用本文： | 严良涛,项晓丽.基于核的k-最近邻在水下目标识别中的应用*[J].应用声学,2019,38(3):448-451. |
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| 作者姓名： | 严良涛 项晓丽 |
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| 作者单位： | 中国人民解放军91388部队,广州杰赛科技股份有限公司 |
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| 基金项目： | 国家自然科学基金项目 (11774374) |
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| 摘 要： | 针对水中目标特征类型多、非线性强的特点,本文将K-KNN应用于水中目标识别。该方法采用PCA对特征矩阵进行降维,利用Kernel技巧将降维后的特征映射到高维空间进行KNN分类识别,并讨论了邻近点个数K对试验结果的影响。实际试验数据验证结果表明：与传统的KNN和BP神经网络分类器相比,K-KNN分类器的综合性能更优。
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| 关 键 词： | 水中目标识别 K-KNN PCA 核函数 |
| 收稿时间： | 2018/10/14 0:00:00 |
| 修稿时间： | 2019/1/23 0:00:00 |
Application of k-NN based on Kernel in underwater target recognition |
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| Yan Liangtao and Xiang Xiaoli.Application of k-NN based on Kernel in underwater target recognition[J].Applied Acoustics,2019,38(3):448-451. |
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| Authors: | Yan Liangtao and Xiang Xiaoli |
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| Institution: | Military Unit 91388 of PLA,Gci Science & Technology Co. Ltd. |
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| Abstract: | The targets underwater have many features and strong nonlinearity, this paper applies K-KNN to underwater target recognition.This method uses PCA to reduce the dimension of the feature matrix. Then the Kernel technique is used to map the reduced dimension to the high-dimensional space for KNN classification and recognition. The influence of the number K of adjacent points on the test results is discussed.The result of actual experimental data show that the K-KNN classifier has better overall performance than the traditional KNN and BP neural network classifiers. |
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| Keywords: | Underwater target recognition K-KNN PCA Kernel |
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