摘要
针对目标雷达散射截面积(RCS)较小、杂波和噪声严重环境下短距离慢动目标难以检测的问题,设计了一种具有跳跃连接结构的双通道卷积自编码器的目标检测方法。该方法以时频谱作为输入,送入设计的卷积自编码器模型中,网络采用IQ双通道结构以提取目标回波的幅度和相位特征,并在中间层实现特征融合。考虑到在时频谱上目标尺度小,网络中还设计了跳跃连接结构将网络顶层与底层跳跃连接以增强目标信号在解码器中的恢复,这种结构还有利于缓解深度网络带来的梯度消散问题,使网络在端对端训练时更加高效。实验证明,相比于传统方法,该方法在杂波和噪声严重的条件下可以获得更好的慢动目标检测效果。
In the environment with small Radar-Cross Section(RCS)serious clutter and noise it is difficult to detect the slow-moving target at short range.To solve the problem a target detection method based on double-channel convolutional auto-encoder with skip connection is proposed.The time-frequency spectrum is introduced to the convolutional auto-encoder as the input.The neural network structure adopts the IQ double-channel to extract amplitude features and phase features from target echoes and fuses the multi-dimensional features in the middle layer.Considering that the target scale is small in time-frequency spectrum the skip connection structure is designed in the network which connects the top and the bottom of the network to improve the recovery ability of target feature in decoders.Moreover it can mitigate the gradient dissipation problem of the deep network and improve the efficiency of end-to-end training.Experimental results show that:In the environment with serious clutter and noise this method can achieve better performance on detection of small slow-moving target than the traditional methods.
作者
扶明
郑霖
杨超
黄凤青
邓小芳
刘争红
FU Ming;ZHENG Lin;YANG Chao;HUANG Fengqing;DENG Xiaofang;LIU Zhenghong(Guangxi Key Laboratory of Wireless Wideband Communications and Signal Processing Guilin University of Electronic Technology,Guilin 541004 China;Science and Technology on Communication Networks Laboratory,Shijiazhuang 050081 China)
出处
《电光与控制》
CSCD
北大核心
2021年第3期1-6,共6页
Electronics Optics & Control
基金
国家自然科学基金(61571143,61761014)
广西重点研究计划(Guike AB18126030)。
关键词
目标检测
慢速运动目标
深度卷积自编码器
特征融合
残差网络
target detection
slow moving target
deep convolutional auto-encoder
feature fusion
residual network
