太赫兹科学与电子信息学报2024，Vol.22Issue(2)：152-159,8.DOI:10.11805/TKYDA2023059
基于改进DeeplabV3+的HFSWR电离层杂波及海杂波自动识别
Automatic recognition method of ionospheric clutter and sea clutter for High Frequency Surface Wave Radar based on improved DeeplabV3+
摘要
Abstract
The background noise in the echo spectrum of High Frequency Surface Wave Radar(HFSWR)is complex,the clutter accounts for a small proportion and the ionospheric clutter has different forms and positions,therefore,it is difficult to automatically recognize the clutters.Based on DeeplabV3+ deep learning algorithm,an automatic identification method of ionospheric clutter and sea clutter is proposed for HFSWR.Selecting the lightweight MobileNetV2 backbone feature network,adding the channel attention mechanism module SENet,the focused learning of clutter labels is realized,and the loss weight of various labels in the training set is optimized.The model pre-training transfer method is employed to pre-train the network to tackle with the problem of too small sample space.The experimental results on the measured data set show that the proposed method can realize the automatic recognition of ionospheric clutter and sea clutter in HFSWR,and can obtain more accurate and finer clutter recognition results than the original DeeplabV3+ algorithm.The mean Intersection over Union(mIoU)and Accuracy(ACC)of sea clutter recognition results are increased by 2.9%and 5.1%respectively,and the mIoU and ACC of ionospheric clutter recognition results are increased by 3.0%and 4.9%respectively.关键词
高频地波雷达/DeeplabV3+算法/通道注意力机制/迁移学习/电离层杂波/海杂波/杂波自动识别Key words
High Frequency Surface Wave Radar/DeeplabV3+/channel attention mechanism/transfer learning/ionospheric clutter/sea clutter/automatic clutter recognition分类
信息技术与安全科学引用本文复制引用
申家维,易建新,万显荣,程丰..基于改进DeeplabV3+的HFSWR电离层杂波及海杂波自动识别[J].太赫兹科学与电子信息学报,2024,22(2):152-159,8.基金项目
国家自然科学基金资助项目(61931015 （）
62071335 （）
62250024) （）
湖北省自然科学基金创新群体资助项目(2021CFA002) （2021CFA002）
