基于随机森林动态集成的水下尺度目标识别

曹涛; 邓剑晶; 岳玲; 李永胜

doi:10.11993/j.issn.2096-3920.2024-0054

基于随机森林动态集成的水下尺度目标识别

doi: 10.11993/j.issn.2096-3920.2024-0054

曹涛¹,
邓剑晶^{2, 3},
岳玲^{2, 3},
李永胜^{2, 3}

1.
海装装备项目管理中心, 北京, 100071
2.
中国船舶集团有限公司第七〇五研究所, 陕西西安, 710077
3.
水下信息与控制全国重点实验室, 陕西西安, 710077

详细信息

作者简介:
曹涛：曹　涛(1976-), 男, 硕士, 高级工程师, 主要研究方向为水声装备科研管理

计量
- 文章访问数: 22
- HTML全文浏览量: 5
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2024-03-19
- 修回日期: 2024-04-16
- 录用日期: 2024-05-07
- 网络出版日期: 2024-05-23

Research on Underwater Acoustic Scale Target Recognition technology based on Random Forest Dynamic Ensemble selection

CAO Tao¹,
DENG Jianjing^{2, 3},
YUE Ling^{2, 3},
LI Yongsheng^{2, 3}

1.
Program Management Centre of Naval Armament Department, Beijing 100036, China
2.
The 705 Research Institute, China Shipbuilding Industry Corporation, Xi’an 710077, China
3.
Science and Technology on Underwater Information and Control Laboratory, Xi’an 710077, China

摘要

摘要: 水下声自导武器可以较好地完成检测、识别并高速打击水下目标的任务。正确识别目标是水下声自导武器攻击敌方目标的关键。文中提出一种基于动态分类集成技术的水下声自导武器实时目标识别方法。利用水下声自导武器主动宽带探测波形照射下目标不同的反射特性, 从目标宽带相关检测输出提取了能量分布和空间分布统计特征, 并构建了基于随机森林的动态集成模型, 在海试数据集中进行训练与测试。仿真分析表明, 文中所提出的动态集成模型识别效果优于其他分类算法, 可以较好地应用于水下声自导武器目标识别中。
- 水下目标识别 /
- 动态集成选择 /
- 随机森林 /
- 人工智能
Abstract: Underwater acoustic homing weapon is a powerful weapon which can detect, identify and attack the target. The Correct identification of the target is the key of the underwater acoustic homing weapon. This study presents a real-time target recognition method for underwater acoustic weapons. Firstly, the statistic feature of energy distribution and spatial distribution are extracted from the output of wideband correlation detection by using the different reflection characteristics of the target irradiated by the active wideband detection waveform of the underwater acoustic homing weapon. Then, a dynamic ensemble selection model based on random forest is constructed, trained and tested on the real dataset. The results show that the dynamic ensemble selection model proposed in this study is better than other classification model and can be applied to underwater acoustic homing weapon recognition.
- underwater target recognition /
- dynamic ensemble selection /
- artificial intelligence /

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图 1 特征可视化结果

Figure 1. Visualization results of features

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图 2 K均值聚类算法描述

Figure 2. k-means clustering algorithm description

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图 3 不同算法识别正确率直方图

Figure 3. Accuracy of different algorithms

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图 4 不同算法ROC曲线比较

Figure 4. ROC curve of different algorithms

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图 5 不同算法F₁值直方图

Figure 5. F₁ value of six algorithms

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图 6 不同k值下算法的正确率

Figure 6. Algorithm accuracy value of different k

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表 1 分类结果混淆矩阵

Table 1. Two-class confusion matrix

真实情况	预测结果
真实情况	正例	反例
正例	N_TP	N_FN
反例	N_FP	N_TN

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表 2 不同算法ROC曲线下面积值

Table 2. The area under the curve value of different algorithms

算法	均值	标准差
决策树	0.881	0.030
k近邻	0.970	0.030
梯度提升树	0.975	0.008
支持向量机	0.965	0.015
随机森林	0.964	0.010
随机森林动态集成	0.976	0.007

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表 3 不同算法的精确率和召回率

Table 3. The Precision value and Recall value of different algorithms

算法	精确率	召回率
决策树	0.892±0.040	0.912±0.016
k近邻	0.930±0.038	0.909±0.025
梯度提升树	0.935±0.029	0.915±0.019
支持向量机	0.875±0.040	0.932±0.020
随机森林	0.938±0.029	0.926±0.016
随机森林动态集成	0.937±0.027	0.944±0.019

下载: 导出CSV

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