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浙江大学学报(工学版)
浙江大学学报(工学版)  2023, Vol. 57 Issue (6): 1215-1223    DOI: 10.3785/j.issn.1008-973X.2023.06.017
计算机与控制工程     
基于深度神经网络的雷达距离超分辨方法
覃承进1(),蒋俊正1,2,*()
1. 桂林电子科技大学 信息与通信学院，广西壮族自治区 桂林 541004
2. 桂林电子科技大学卫星导航定位与位置服务国家地方联合工程研究中心，广西壮族自治区 桂林 541004
Radar range super-resolution method based on deep neural network
Cheng-jin QIN1(),Jun-zheng JIANG1,2,*()
1. School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
2. Satellite Navigation Positioning and Location Service National and Local Joint Engineering Research Center, Guilin University of Electronic Technology, Guilin 541004, China
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摘要：

现有的雷达超分辨算法难以有效地应用于现实场景, 为此提出基于深度神经网络(DNN)的调频连续波(FMCW)雷达距离超分辨方法. 所提方法通过DNN外推雷达信号的观测时间以提高频域分辨率, 进而提高雷达的距离分辨率. 为了降低后续DNN的处理复杂度，利用快速傅里叶变换结合离散时间傅里叶变换(FFT+DTFT)算法预处理雷达的中频信号. 采用具有非线性拟合能力的DNN对输入信号进行特征提取, 预测信号的发展趋势. 将预测信号递归输入DNN以不断外推时域信号的长度, 对时域外推后的信号进行快速傅里叶变换得到具有高分辨率的距离像. 为了排除杂波干扰, 对距离像进行恒虚警率(CFAR)检测以有效地提取目标的距离信息. 仿真实验结果表明, 所提方法可以超越雷达带宽的限制实现距离超分辨. 与现有的超分辨方法相比, 所提方法具有更小的误差且更适用于处理现实场景中的雷达信号.
关键词： 调频连续波(FMCW)雷达;  深度神经网络(DNN);  距离超分辨;  信号外推;  快速傅里叶变换    
Abstract:

Existing radar super-resolution algorithms are difficult to be effectively applied to real scenes. To resolve this problem, a range super-resolution method for frequency modulated continuous wave (FMCW) radar based on deep neural network (DNN) was proposed. The proposed method extrapolated the observation time of the radar signal by DNN to improve the resolution in frequency domain, so as to enhance the range resolution of the radar. Firstly, fast Fourier transform combined with discrete time Fourier transform (FFT+DTFT) algorithm was utilized to preprocess the intermediate frequency signal of radar for reducing the processing complexity of subsequent DNN. Then, the feature extraction of the input signal was realized by DNN with nonlinear fitting ability, and the development trend of the signal was predicted. Next, the predicted signal was recursively input to DNN to continuously extrapolate the length of the time domain signal. Accordingly, the high-resolution range profile could be obtained by performing fast Fourier transform on the time domain extrapolated signal. Finally, in order to eliminate clutter interference, constant false alarm rate (CFAR) detection was performed on the range profile to effectively extract the target range information. Simulation results show that the proposed method achieves range super-resolution, surpassing the limitation of radar bandwidth. Compared with the existing super-resolution methods, the proposed method has smaller error and is more suitable for processing radar signal in real scenes.
Key words: frequency modulated continuous wave (FMCW) radar    deep neural network (DNN)    range super-resolution    signal extrapolation    fast Fourier transform
收稿日期: 2022-08-15 出版日期: 2023-06-30
CLC:  TN 958.94  
基金资助: 国家自然科学基金资助项目(62171146, 61761011); 广西创新驱动发展专项(桂科AA21077008)
通讯作者: 蒋俊正     E-mail: 20022303111@mails.guet.edu.cn;jzjiang@guet.edu.cn
作者简介: 覃承进（1998—），男，硕士生，从事雷达信号处理理论与应用研究. orcid. org/0000-0003-4731-1554.E-mail： 20022303111@mails.guet.edu.cn
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引用本文:

覃承进,蒋俊正. 基于深度神经网络的雷达距离超分辨方法[J]. 浙江大学学报(工学版), 2023, 57(6): 1215-1223.

Cheng-jin QIN,Jun-zheng JIANG. Radar range super-resolution method based on deep neural network. Journal of ZheJiang University (Engineering Science), 2023, 57(6): 1215-1223.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.06.017    或    https://www.zjujournals.com/eng/CN/Y2023/V57/I6/1215

图 1  外推信号的时域图和频域图
图 2  基于深度神经网络的雷达距离超分辨方法的信号处理流程图
图 3  雷达正面和实测场景图
参数 数值
起始频率 ${f_{\rm{c}}}/{\rm{GHz} }$ 77
调频斜率 $ S/({\rm{MHz}} \cdot \text{μ} {{\rm{s}}^{ - 1}}) $ 25
调频时间 $T/\text{μs}$ 30
采样点数 $N$ 128
采样率 ${f_{\rm{s} } }/(10^{6}\;{\rm{s} }^{-1})$ 5
表 1  雷达实测参数设置
图 4  不同超分辨方法处理实测数据1的结果图
图 5  不同超分辨方法处理实测数据2的结果图
图 6  实测数据1的原始信号和外推信号的距离-速度图对比
图 7  实测数据2的原始信号和外推信号的距离-速度图对比
算法 ${{\rm{MAE}}_{ {\rm{M1} } }},{{\rm{RMSE}}_{ {\rm{M1} } }}$ ${{\rm{MAE}} _{\rm{M2}}},{\rm{RMSE}} _{\rm{M2} }$
$ \alpha = 2 $ $ \alpha = 3 $ $ \alpha = 4 $ $ \alpha = 2 $ $ \alpha = 3 $ $ \alpha = 4 $
EDFT 0.0847, 0.1083 0.2145, 0.2518 0.3456, 0.4010 0.3187, 0.4178 0.3486, 0.4211 0.2500, 0.3402
Fast-MUSIC 0.4640, 0.5551 0.4279, 0.5099 0.4121, 0.5291 0.4318, 0.5419 0.3731, 0.4599 0.3652, 0.4614
本研究 0.1926, 0.2134 0.1784, 0.2580 0.2426, 0.3280 0.2169, 0.2614 0.3165, 0.3513 0.2427, 0.2989
表 2  实测数据的距离像在不同外推长度下的误差
算法 ${{\rm{MAE}}_ {\rm{S1} }}, {{\rm{RMSE}}_ {\rm{S1} }}$ ${{\rm{MAE}}_{\rm{S2} }}, {{\rm{RMSE}}_ {\rm{S2} } }$
$ \alpha = 2 $ $ \alpha = 3 $ $ \alpha = 4 $ $ \alpha = 2 $ $ \alpha = 3 $ $ \alpha = 4 $
AR模型 0.0228, 0.0386 0.0315, 0.0487 0.0559, 0.1016 0.1174, 0.1425 0.2591, 0.3135 0.4397, 0.4887
EDFT 0.0289, 0.0384 0.0369, 0.0421 0.0502, 0.0728 0.1844, 0.2236 0.2010, 0.2667 0.1758/0.2479
Fast-MUSIC 0.1275, 0.2742 0.1050, 0.2149 0.1107, 0.2280 0.2736, 0.3700 0.2235, 0.2903 0.1698, 0.2507
本研究 0.0631, 0.1042 0.0580, 0.0715 0.0559, 0.1042 0.1314, 0.1617 0.1651, 0.2295 0.1256, 0.1937
表 3  仿真数据1和仿真数据2的距离像在不同外推长度下的误差
算法 ${{\rm{MAE}}_{\rm{S3} }}, {{\rm{RMSE}}_{\rm{S3} } }$ ${{\rm{MAE}}_{\rm{S4} }}, {{\rm{RMSE}}_{\rm{S4} }}$
$ \alpha = 2 $ $ \alpha = 3 $ $ \alpha = 4 $ $ \alpha = 2 $ $ \alpha = 3 $ $ \alpha = 4 $
AR模型 0.2142, 0.2733 0.2305, 0.2802 0.2849, 0.3410 0.1459, 0.1749 0.2203, 0.3063 0.2272, 0.3177
EDFT 0.2697, 0.3562 0.2483, 0.3312 0.2298, 0.3192 0.1544, 0.2024 0.2067, 0.2838 0.2172, 0.3069
Fast-MUSIC 0.2897, 0.3491 0.2877, 0.3512 0.2379, 0.3487 0.2782, 0.3642 0.2862, 0.3682 0.2901, 0.3805
本文方法 0.2693, 0.3357 0.1719, 0.2598 0.1482, 0.2225 0.1314, 0.1617 0.1651, 0.2295 0.1928, 0.2589
表 4  仿真数据3和仿真数据4的距离像在不同外推长度下的误差
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