• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于FMCW传感器的低复杂度时域测距算法

Low-Complexity Time-Domain Ranging Algorithm with FMCW Sensors.

作者信息

Pan Xi, Xiang Chengyong, Liu Shouliang, Yan Shuo

机构信息

School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Beijing Research Institute of Telemetry, Beijing 100076, China.

出版信息

Sensors (Basel). 2019 Jul 19;19(14):3176. doi: 10.3390/s19143176.

DOI:10.3390/s19143176
PMID:31330939
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6679248/
Abstract

A time-domain ranging algorithm is proposed for a frequency-modulated continuous wave (FMCW) short-range radar sensor with high accuracy and low complexity. The proposed algorithm estimates the distance by calculating the ratio of the beat frequency signal to its derivative and thereby eliminates the restriction of frequency bandwidth on ranging accuracy. Meanwhile, we provide error analysis of the proposed algorithm under different distances, integral lengths, relative velocities, and signal-to-noise ratios (SNRs). Finally, we fabricate FMCW sensor prototype and construct a measurement system. Testing results demonstrate that the proposed time-domain algorithm could achieve range error within 0.8 m. Compared with the conventional fast Fourier transform (FFT) estimation scheme, the proposed method performs ranging without the requirement of complex multiplications, which makes it reasonable to be implemented in real-time and low-cost systems.

摘要

针对一种高精度、低复杂度的调频连续波(FMCW)短程雷达传感器,提出了一种时域测距算法。该算法通过计算拍频信号与其导数的比值来估计距离,从而消除了频率带宽对测距精度的限制。同时,我们给出了该算法在不同距离、积分长度、相对速度和信噪比(SNR)下的误差分析。最后,我们制作了FMCW传感器原型并构建了一个测量系统。测试结果表明,所提出的时域算法能够实现0.8米以内的测距误差。与传统的快速傅里叶变换(FFT)估计方案相比,该方法在测距时无需复杂乘法运算,这使得它在实时和低成本系统中实现具有合理性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/0b9b2952d750/sensors-19-03176-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/4e900b299a5b/sensors-19-03176-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/8fcaacaeeb6d/sensors-19-03176-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/e48468d764a1/sensors-19-03176-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/9002b2893553/sensors-19-03176-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/d08d6c8076b5/sensors-19-03176-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/1bfc9d87cf29/sensors-19-03176-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/cccf1b8b2cf9/sensors-19-03176-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/513208b1e9f5/sensors-19-03176-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/de9e9cffb536/sensors-19-03176-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/2fb435a73a42/sensors-19-03176-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/0b9b2952d750/sensors-19-03176-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/4e900b299a5b/sensors-19-03176-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/8fcaacaeeb6d/sensors-19-03176-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/e48468d764a1/sensors-19-03176-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/9002b2893553/sensors-19-03176-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/d08d6c8076b5/sensors-19-03176-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/1bfc9d87cf29/sensors-19-03176-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/cccf1b8b2cf9/sensors-19-03176-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/513208b1e9f5/sensors-19-03176-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/de9e9cffb536/sensors-19-03176-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/2fb435a73a42/sensors-19-03176-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e232/6679248/0b9b2952d750/sensors-19-03176-g011.jpg

相似文献

1
Low-Complexity Time-Domain Ranging Algorithm with FMCW Sensors.基于FMCW传感器的低复杂度时域测距算法
Sensors (Basel). 2019 Jul 19;19(14):3176. doi: 10.3390/s19143176.
2
The Role of Millimeter-Waves in the Distance Measurement Accuracy of an FMCW Radar Sensor.毫米波在 FMCW 雷达传感器距离测量精度中的作用。
Sensors (Basel). 2019 Sep 12;19(18):3938. doi: 10.3390/s19183938.
3
A Low-Complexity FMCW Surveillance Radar Algorithm Using Two Random Beat Signals.使用两个随机拍频信号的低复杂度 FMCW 监测雷达算法。
Sensors (Basel). 2019 Jan 31;19(3):608. doi: 10.3390/s19030608.
4
FMCW LiDAR System to Reduce Hardware Complexity and Post-Processing Techniques to Improve Distance Resolution.用于降低硬件复杂度的调频连续波激光雷达系统及用于提高距离分辨率的后处理技术。
Sensors (Basel). 2020 Nov 22;20(22):6676. doi: 10.3390/s20226676.
5
Low-Complexity Joint Range and Doppler FMCW Radar Algorithm Based on Number of Targets.基于目标数的低复杂度联合距离和多普勒 FMCW 雷达算法。
Sensors (Basel). 2019 Dec 20;20(1):51. doi: 10.3390/s20010051.
6
High-Efficiency Super-Resolution FMCW Radar Algorithm Based on FFT Estimation.基于快速傅里叶变换(FFT)估计的高效超分辨率调频连续波(FMCW)雷达算法
Sensors (Basel). 2021 Jun 10;21(12):4018. doi: 10.3390/s21124018.
7
FMCW Radar Estimation Algorithm with High Resolution and Low Complexity Based on Reduced Search Area.基于缩小搜索区域的高分辨率低复杂度调频连续波雷达估计算法
Sensors (Basel). 2022 Feb 5;22(3):1202. doi: 10.3390/s22031202.
8
A Low-Power High-Accuracy Urban Waterlogging Depth Sensor Based on Millimeter-Wave FMCW Radar.一种基于毫米波调频连续波雷达的低功耗高精度城市内涝深度传感器。
Sensors (Basel). 2022 Feb 6;22(3):1236. doi: 10.3390/s22031236.
9
High-precision frequency estimation for frequency modulated continuous wave laser ranging using the multiple signal classification method.基于多重信号分类法的调频连续波激光测距高精度频率估计
Appl Opt. 2017 Aug 20;56(24):6956-6961. doi: 10.1364/AO.56.006956.
10
A Novel DFT-Based DOA Estimation by a Virtual Array Extension Using Simple Multiplications for FMCW Radar.一种基于新型 DFT 的 DOA 估计方法,通过使用简单乘法对 FMCW 雷达进行虚拟阵扩展。
Sensors (Basel). 2018 May 14;18(5):1560. doi: 10.3390/s18051560.

引用本文的文献

1
An Accurate Altimetry Method for High-Altitude Airburst Fuze Based on Two-Dimensional Joint Extension Characteristics.一种基于二维联合扩展特性的高空气爆引信精确测高方法。
Sensors (Basel). 2025 Apr 6;25(7):2329. doi: 10.3390/s25072329.
2
Low-Complexity Joint Range and Doppler FMCW Radar Algorithm Based on Number of Targets.基于目标数的低复杂度联合距离和多普勒 FMCW 雷达算法。
Sensors (Basel). 2019 Dec 20;20(1):51. doi: 10.3390/s20010051.

本文引用的文献

1
A Low-Complexity FMCW Surveillance Radar Algorithm Using Two Random Beat Signals.使用两个随机拍频信号的低复杂度 FMCW 监测雷达算法。
Sensors (Basel). 2019 Jan 31;19(3):608. doi: 10.3390/s19030608.
2
3D Target Localization of Modified 3D MUSIC for a Triple-Channel K-Band Radar.三维 MUSIC 改进算法的三通道 K 波段雷达三维目标定位
Sensors (Basel). 2018 May 20;18(5):1634. doi: 10.3390/s18051634.
3
A Novel DFT-Based DOA Estimation by a Virtual Array Extension Using Simple Multiplications for FMCW Radar.一种基于新型 DFT 的 DOA 估计方法,通过使用简单乘法对 FMCW 雷达进行虚拟阵扩展。
Sensors (Basel). 2018 May 14;18(5):1560. doi: 10.3390/s18051560.
4
A Pedestrian Detection Scheme Using a Coherent Phase Difference Method Based on 2D Range-Doppler FMCW Radar.一种基于二维距离-多普勒调频连续波雷达的相干相位差法行人检测方案。
Sensors (Basel). 2016 Jan 20;16(1):124. doi: 10.3390/s16010124.