Background Classification Method for Marine Target Detection Based on CNN

XU Ya-nan; LIU Ning-bo; DING Hao; GUAN Jian; HUANG Yong

doi:10.3969/j.issn.0372-2112.2019.12.008

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Background Classification Method for Marine Target Detection Based on CNN

更新时间：2025-07-16

- Background Classification Method for Marine Target Detection Based on CNN
- Acta Electronica Sinica Vol. 47, Issue 12, Pages: 2505-2514(2019)
- 作者机构：
  
  海军航空大学信息融合研究所,山东,烟台,264001
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61871392, No.61531020, No.61871391);China Postdoctoral Science Foundation (No.2017M620862)
- DOI：10.3969/j.issn.0372-2112.2019.12.008
  CLC： TN957.51
- Published Online：25 December 2019，
  
  Published：2019
- 稿件说明：
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XU Ya-nan, LIU Ning-bo, DING Hao, et al. Background Classification Method for Marine Target Detection Based on CNN[J]. Acta Electronica Sinica, 2019, 47(12): 2505-2514.
DOI：

XU Ya-nan, LIU Ning-bo, DING Hao, et al. Background Classification Method for Marine Target Detection Based on CNN[J]. Acta Electronica Sinica, 2019, 47(12): 2505-2514. DOI： 10.3969/j.issn.0372-2112.2019.12.008.

摘要

该文主要研究基于卷积神经网络（Convolutional Neural Networks，CNN）的海上目标探测背景分类方法.以CNN中的经典网络LeNet为例，基于IPIX雷达实测数据集，进行控制变量的模型训练，对分类准确率、训练速度、一维信号的二维特征图变化等进行分析，基于实测数据集验证了利用CNN在一维雷达回波信号中进行海杂波与噪声分类的可行性，并同步分析了数据预处理、单个样本序列长度、网络结构参数等影响因素对分类准确率的影响，并针对典型探测场景分类进行了验证.结果表明，LeNet卷积神经网络在海上探测背景区分方面，具有很高的分类准确率，并且数据预处理方式、单个样本序列长度对结果影响显著，而网络结构参数有一定的调节区间，在此区间内调整，影响不显著，所提方法在顺/逆浪向、高/低海况条件下杂波分类与杂噪分类方面具有很高的准确率.

Abstract

In this paper

the background classification method of marine target detection based on convolutional neural network (CNN) is mainly studied. Taking LeNet as an example

based on the IPIX measured data set

the model training through controlling variables is carried out. The feasibility of using CNN in the classification of sea clutter and noise in one dimensional radar echo signal is studied

and the influence of factors such as data preprocessing

single sample sequence length and network structure parameters on classification accuracy is analyzed synchronously

and verified for the typical detection scene classification. The application results of measured data show that the proposed method has high accuracy in clutter classification and noise classification under the conditions of forward/reverse direction and high/low sea conditions.

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