SNR and Resolution Improvement Algorithm With the Concatenation of Multiple Chirps for FMCW Radar

被引:5
作者
Kim, Bong-seok [1 ]
Lee, Jonghun [1 ,2 ]
Kim, Sangdong [1 ,2 ]
机构
[1] Daegu Gyeongbuk Inst Sci & Technol DGIST, Div Automot Technol, Daegu 42988, South Korea
[2] DGIST, Dept Interdisciplinary Engn, Daegu 42988, South Korea
来源
IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS | 2024年 / 23卷 / 01期
基金
新加坡国家研究基金会;
关键词
Chirp; Signal resolution; Signal to noise ratio; Radar; Estimation; Bandwidth; Correlation; Concatenation; fast Fourier transform (FFT); frequency-modulated continuous-wave (FMCW); high resolution; NEURAL-NETWORK;
D O I
10.1109/LAWP.2023.3317872
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This letter proposes an algorithm that improves the signal-to-noise ratio (SNR) and resolution of frequency-modulated continuous-wave (FMCW) radar systems by using concatenation among multiple chirp signals. In FMCW radar systems, increasing the bandwidth is necessary to improve the range resolution. The proposed algorithm enhances the frequency estimation resolution without the use of additional bandwidth by concatenating short chirp signals to create longer signals while also improving the SNR by combining multiple chirps with different noise components. To prevent discontinuities during the concatenation process, the proposed algorithm uses cross-correlation between the end of one chirp and the beginning of another. Simulation and experimental results demonstrate the effectiveness of the proposed algorithm in improving both the SNR and resolution.
引用
收藏
页码:84 / 88
页数:5
相关论文
共 32 条
[21]   Encoding physics to learn reaction-diffusion processes [J].
Rao, Chengping ;
Ren, Pu ;
Wang, Qi ;
Buyukozturk, Oral ;
Sun, Hao ;
Liu, Yang .
NATURE MACHINE INTELLIGENCE, 2023, 5 (7) :765-779
[22]   A Physics-Based Neural-Network Way to Perform Seismic Full Waveform Inversion [J].
Ren, Yuxiao ;
Xu, Xinji ;
Yang, Senlin ;
Nie, Lichao ;
Chen, Yangkang .
IEEE ACCESS, 2020, 8 :112266-112277
[23]   Inverse design of plasma metamaterial devices with realistic elements [J].
Rodriguez, Jesse A. ;
Cappelli, Mark A. .
JOURNAL OF PHYSICS D-APPLIED PHYSICS, 2022, 55 (46)
[24]   A Method for dSTEC Interpolation: Ionosphere Kernel Estimation Algorithm [J].
Shi, Zenghui ;
Zhi, Nan ;
Fu, Haiyang ;
Wang, Denghui ;
Sui, Yun ;
Zhao, Yi ;
Feng, Shaojun ;
Jin, Ya-Qiua .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
[25]   Sparse Reconstruction of 3-D Regional Ionospheric Tomography Using Data From a Network of GNSS Reference Stations [J].
Sui, Yun ;
Fu, Haiyang ;
Wang, Denghui ;
Xu, Feng ;
Feng, Shaojun ;
Cheng, Jin ;
Jin, Ya-Qiu .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
[26]   Deep-Learning Schemes for Full-Wave Nonlinear Inverse Scattering Problems [J].
Wei, Zhun ;
Chen, Xudong .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 57 (04) :1849-1860
[27]   Three-Dimensional Scattering and Inverse Scattering From a Disturbed Region in Planarly Layered Cold Unmagnetized Plasma Media [J].
Wen, Paiju ;
Chen, Yongjin ;
Han, Feng ;
Liu, Na ;
Liu, Hai ;
Liu, Qing Huo .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2017, 14 (04) :559-563
[28]   Radiation Theory of the Plasma Antenna [J].
Ye, Huan Qing ;
Gao, Min ;
Tang, Chang Jian .
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2011, 59 (05) :1497-1502
[29]   FDTD Analysis of Propagation and Absorption in Nonuniform Anisotropic Magnetized Plasma Slab [J].
Zhang, Jing ;
Fu, Haiyang ;
Scales, Wayne .
IEEE TRANSACTIONS ON PLASMA SCIENCE, 2018, 46 (06) :2146-2153
[30]   Physics-Informed Deep Neural Network for Inhomogeneous Magnetized Plasma Parameter Inversion [J].
Zhang, Yangyang ;
Fu, Haiyang ;
Qin, Yilan ;
Wang, Kangning ;
Ma, Jiayu .
IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, 2022, 21 (04) :828-832