多分类器联合虚警可控的海上小目标检测方法

薛安克; 毛克成; 张乐

doi:10.11999/JEIT220710

多分类器联合虚警可控的海上小目标检测方法

doi: 10.11999/JEIT220710

杭州电子科技大学自动化学院杭州 310018

详细信息

作者简介:
薛安克：男，教授，研究方向为智能信息处理与信息融合

毛克成：男，硕士生，研究方向为海杂波环境下的小目标检测

张乐：女，高级实验师，研究方向为智能信息处理与智能检测

通讯作者:
张乐　lezhang@hdu.edu.cn

中图分类号: TN957.51
计量
- 文章访问数: 380
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- PDF下载量: 120
- 被引次数: 0
出版历程
- 收稿日期: 2022-05-31
- 修回日期: 2022-10-15
- 网络出版日期: 2022-10-20
- 刊出日期: 2023-07-10

Multi-feature Marine Small Target Detection Based on Multi-class Classifier

Schlool of Automation, Hangzhou Dianzi University, Hangzhou 310018, China

摘要

摘要: 模式识别技术已经广泛应用于海上目标检测，其中二分类的模式识别算法在处理该问题时会面临类别非均衡的困境。传统方法一般通过添加人工仿真目标回波扩充目标数据集，检测结果容易受到仿真精度的影响，且增加算法的复杂度。该文提出一种基于多分类思想的多特征海上小目标智能检测方法，先对海杂波数据与目标数据进行多维特征提取，构建高维特征空间；再基于多分类思想中的“1对1”方法，将海杂波特征空间划分成多个子空间，每个杂波子空间与目标数据特征空间等大，构造多个二分类器进行联合判决。该文选取的二分类器为改进的双参数K近邻 (K-NN)算法，可有效调节虚警率。经冰多参数成像X波段雷达(IPIX)数据集验证，所提方法在观测时间为1.024 s时获得了82.40%的检测概率，与基于K-NN的检测器做比较，获得了2%的性能提升。
- 海杂波 /
- 小目标检测 /
- 多分类 /
- 双参数寻优K近邻(K-NN)算法 /
- 可控虚警
Abstract: The pattern recognition technology have been widely used in target detection within sea clutter, and the binary pattern recognition algorithm will face the dilemma of catgory disequilibrium when dealing with this problem. The traditional method expands the target data set by adding artificial simulated target echoes, however,the detection result is easily affected by the accuracy of simulation data, and the complexity of the algorithm increases.In this paper, a multi-feature intelligent detection method for small targets within sea clutter based on multi-class classifier is proposed. Firstly, multi-dimensional features are extracted from sea clutter and target data to construct a high-dimensional feature space. Then, based on the “one to one” method of multi-class classification, the sea clutter feature space is divided into multiple subspaces, which is as large as the target data feature space to biuld multiple binary classifiers for joint decision. The binary classifier selected in this paper is the improved two-parameter K-Nearest Neighbor (K-NN) algorithm, which can effectively adjust the false alarm rate. Verified by Ice MultiParameter Imaging X-band radar (IPIX) radar data set, the detection probability of the proposed method is 82.40% when the observation time is 1.024 s, and the performance of the proposed method is improved by 2% compared with the existing feature detectors of the same type.
- Sea clutter /
- Small target detection /
- Multi-class classification /
- Two-parameter optimization K-Nearest Neighbor(K-NN) algorithm /
- Controllable false alarm

HTML全文

图 1 多分类方法“1对多”与“1对1”

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图 2 多分类器联合判决方法

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图 3 对于单个二分类器，参数对虚警率的影响

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图 4 $G \cdot {P_{{\text{fa}}}} < 1$时，参数对虚警率的影响

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图 5 智能检测器流程图

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图 6 所提检测器与其余检测器的检测概率

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图 7 所提检测器在不同观测时间下的检测概率对比

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图 8 所提检测器的ROC曲线

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图 9 基于6特征的检测器在IPIX数据集上的性能损失对比(10^–3虚警率下)

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表 1 1993年IPIX数据集说明

序号	数据名称	浪高(m)	风速(km/h)	目标所在单元	受影响单元
(1)	#17	2.2	9	9	8,10,11
(2)	#26	1.1	9	7	6,8
(3)	#30	0.9	19	7	6,8
(4)	#31	0.9	19	7	6,8,9
(5)	#40	1.0	9	7	5,6,8
(6)	#54	0.7	20	8	7,9,10
(7)	#280	1.6	10	8	7,10
(8)	#310	0.9	33	7	6,8,9
(9)	#311	0.9	33	7	6,8,9
(10)	#320	0.9	25	7	6,8,9

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表 2 IPIX数据集上多种检测器的检测概率

检测器	观测时间(s)	虚警率	平均检测概率
基于三特征的检测器^[17]	1.024	0.001	0.567
基于时频三特征的检测器^[18]	1.024	0.001	0.677
基于K-NN的检测器^[19]	1.024	0.001	0.807
本文所提检测器	1.024	0.001	0.824

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