Deep-Learning-Based Velocity Estimation for FMCW Radar With Random Pulse Position Modulation

被引：2

作者：

Sang, Tzu-Hsien ^{[1
]}

Tseng, Kuan-Yu ^{[1
]}

Chien, Feng-Tsun ^{[1
]}

Chang, Chia-Chih ^{[1
]}

Peng, Yi-Hsin ^{[1
]}

Guo, Jiun-In ^{[1
]}

机构：

[1] Natl Yang Ming Chiao Tung Univ, Inst Elect, Hsinchu 300, Taiwan

来源：

IEEE SENSORS LETTERS | 2022年 / 6卷 / 03期

关键词：

Chirp; Radar; Estimation; Interference; Covariance matrices; Sensors; Convolutional neural networks; Sensor signal processing; radar sensors; advance driver assistance systems (ADAS); deep learning (DL); frequency modulated continuous wave (FMCW) radar; pulse position modulation (PPM); radar-to-radar interference; sparse signal; velocity estimation; MUTUAL INTERFERENCE;

D O I：

10.1109/LSENS.2022.3156882

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：

As autonomous driving technology progresses forward, frequency modulated continuous wave (FMCW) radar is projected to be used more widely for automotive purposes. Due to the expected rapid growth of road vehicles equipped with radars, more attention is paid to finding ways of reducing mutual interference among automotive radars. In this letter, a novel scheme is proposed to solve the issue of velocity estimation for FMCW radar with random pulse position modulation, which is a promising technique to drastically mitigate mutual interference. The proposed scheme uses a two-dimensional convolutional neural network working on covariance matrices of signals extracted from the region of interest as well as the information of chirp positions. Analysis of its performance, in particular comparison with that of orthogonal matching pursuit, with simulation and experimental data demonstrate the potential of the approach.

引用

页数：4

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