摘要
强杂波背景下的慢速目标检测存在低多普勒频移、杂波干扰严重、鲁棒性不足、特征提取困难与信息利用不充分等问题。为此,提出一种基于深度自编码网络的宽带信号目标检测方法。利用时频变换解析回波信息,通过深度自编码网络算法,在时频域提取针对目标的深度抽象信息进行目标检测,以准确感知环境变化。仿真结果表明,与支持向量机、超限学习机和后向传播神经网络等传统机器学习相比,该方法可以有效感知环境变化,具有较高的鲁棒性和检测性能。
The slow target detection in the background of strong clutter has such problems as low Doppler frequency shift,clutter interference,lack of robustness,feature extraction difficulties and inadequate information utilization.Therefore,a target detection method of wideband signal based on deep self-coding network is proposed.The echo information is analyzed by using time-frequency transform,and the deep self-coding network algorithm is used to extract the target deep abstract information in the time-frequency domain for target detection to accurately sense the environmental change.Simulation results show that compared with traditional machine learning such as Support Vector Machine（SVM）,Extreme Learning Machine（ELM） and Back Propagation Neural Network（BPNN）,the proposed method can effectively detect environmental changes and has high robustness and detection performance.
出处
《计算机工程》
CAS
CSCD
北大核心
2018年第2期129-134,共6页
Computer Engineering
基金
国家自然科学基金(61362006
61571143)
广西无线宽带通信与信号处理重点实验室基金(GXKL061501)
广西自然科学基金(2014GXNSFBA118288
2014GXNSFAA118387)
关键词
目标检测
深度学习
自编码神经网络
特征提取
机器学习
target detection
deep learning
self-coding neural network
feature extraction
machine learning
