The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
La surveillance intelligente du trafic fournit un support informationnel pour la conduite autonome, largement utilisée dans les systèmes de transport intelligents (STI). Une méthode d'estimation des paramètres de cibles mobiles de véhicules basée sur des radars à ondes millimétriques est proposée pour résoudre le problème de faible précision de détection due à l'ambiguïté de vitesse et au couplage Doppler-angle dans le processus de surveillance du trafic. Tout d’abord, un réseau d’antennes MIMO avec des éléments qui se chevauchent est construit en les introduisant dans la conception typique des antennes de réseau radar MIMO. Les erreurs de phase induites par le mouvement sont éliminées par la différence de phase entre les éléments qui se chevauchent. Ensuite, les erreurs de position parmi elles sont corrigées via une méthode itérative et l’angle de plusieurs cibles est estimé. Enfin, la levée de l'ambiguïté de la vitesse est réalisée en adoptant la différence de phase corrigée des erreurs entre les éléments qui se chevauchent. Une estimation précise de l'angle et de la vitesse de la cible mobile du véhicule est obtenue. Grâce aux expériences de simulation de Monte Carlo, l'erreur d'angle est de 0.1° et l'erreur de vitesse est de 0.1 m/s. Les résultats de la simulation montrent que la méthode peut être utilisée pour résoudre efficacement les problèmes liés à l'ambiguïté de vitesse et au couplage Doppler-angle, tandis que la précision de l'estimation de la vitesse et de l'angle peut être améliorée. Un algorithme amélioré est testé sur les ensembles de données de véhicules qui sont rassemblés vers l'avant des scènes publiques ordinaires d'une ville. Les résultats expérimentaux vérifient en outre la faisabilité de la méthode, qui répond aux exigences de temps réel et de précision des ITS en matière de surveillance des informations sur les véhicules.
Feng TIAN
Xi'an University of Science and Technology
Wan LIU
Xi'an University of Science and Technology
Weibo FU
Xi'an University of Science and Technology
Xiaojun HUANG
Xi'an University of Science and Technology
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copier
Feng TIAN, Wan LIU, Weibo FU, Xiaojun HUANG, "Motion Parameter Estimation Based on Overlapping Elements for TDM-MIMO FMCW Radar" in IEICE TRANSACTIONS on Communications,
vol. E106-B, no. 8, pp. 705-713, August 2023, doi: 10.1587/transcom.2022EBP3088.
Abstract: Intelligent traffic monitoring provides information support for autonomous driving, which is widely used in intelligent transportation systems (ITSs). A method for estimating vehicle moving target parameters based on millimeter-wave radars is proposed to solve the problem of low detection accuracy due to velocity ambiguity and Doppler-angle coupling in the process of traffic monitoring. First of all, a MIMO antenna array with overlapping elements is constructed by introducing them into the typical design of MIMO radar array antennas. The motion-induced phase errors are eliminated by the phase difference among the overlapping elements. Then, the position errors among them are corrected through an iterative method, and the angle of multiple targets is estimated. Finally, velocity disambiguation is performed by adopting the error-corrected phase difference among the overlapping elements. An accurate estimation of vehicle moving target angle and velocity is achieved. Through Monte Carlo simulation experiments, the angle error is 0.1° and the velocity error is 0.1m/s. The simulation results show that the method can be used to effectively solve the problems related to velocity ambiguity and Doppler-angle coupling, meanwhile the accuracy of velocity and angle estimation can be improved. An improved algorithm is tested on the vehicle datasets that are gathered in the forward direction of ordinary public scenes of a city. The experimental results further verify the feasibility of the method, which meets the real-time and accuracy requirements of ITSs on vehicle information monitoring.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2022EBP3088/_p
Copier
@ARTICLE{e106-b_8_705,
author={Feng TIAN, Wan LIU, Weibo FU, Xiaojun HUANG, },
journal={IEICE TRANSACTIONS on Communications},
title={Motion Parameter Estimation Based on Overlapping Elements for TDM-MIMO FMCW Radar},
year={2023},
volume={E106-B},
number={8},
pages={705-713},
abstract={Intelligent traffic monitoring provides information support for autonomous driving, which is widely used in intelligent transportation systems (ITSs). A method for estimating vehicle moving target parameters based on millimeter-wave radars is proposed to solve the problem of low detection accuracy due to velocity ambiguity and Doppler-angle coupling in the process of traffic monitoring. First of all, a MIMO antenna array with overlapping elements is constructed by introducing them into the typical design of MIMO radar array antennas. The motion-induced phase errors are eliminated by the phase difference among the overlapping elements. Then, the position errors among them are corrected through an iterative method, and the angle of multiple targets is estimated. Finally, velocity disambiguation is performed by adopting the error-corrected phase difference among the overlapping elements. An accurate estimation of vehicle moving target angle and velocity is achieved. Through Monte Carlo simulation experiments, the angle error is 0.1° and the velocity error is 0.1m/s. The simulation results show that the method can be used to effectively solve the problems related to velocity ambiguity and Doppler-angle coupling, meanwhile the accuracy of velocity and angle estimation can be improved. An improved algorithm is tested on the vehicle datasets that are gathered in the forward direction of ordinary public scenes of a city. The experimental results further verify the feasibility of the method, which meets the real-time and accuracy requirements of ITSs on vehicle information monitoring.},
keywords={},
doi={10.1587/transcom.2022EBP3088},
ISSN={1745-1345},
month={August},}
Copier
TY - JOUR
TI - Motion Parameter Estimation Based on Overlapping Elements for TDM-MIMO FMCW Radar
T2 - IEICE TRANSACTIONS on Communications
SP - 705
EP - 713
AU - Feng TIAN
AU - Wan LIU
AU - Weibo FU
AU - Xiaojun HUANG
PY - 2023
DO - 10.1587/transcom.2022EBP3088
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2023
AB - Intelligent traffic monitoring provides information support for autonomous driving, which is widely used in intelligent transportation systems (ITSs). A method for estimating vehicle moving target parameters based on millimeter-wave radars is proposed to solve the problem of low detection accuracy due to velocity ambiguity and Doppler-angle coupling in the process of traffic monitoring. First of all, a MIMO antenna array with overlapping elements is constructed by introducing them into the typical design of MIMO radar array antennas. The motion-induced phase errors are eliminated by the phase difference among the overlapping elements. Then, the position errors among them are corrected through an iterative method, and the angle of multiple targets is estimated. Finally, velocity disambiguation is performed by adopting the error-corrected phase difference among the overlapping elements. An accurate estimation of vehicle moving target angle and velocity is achieved. Through Monte Carlo simulation experiments, the angle error is 0.1° and the velocity error is 0.1m/s. The simulation results show that the method can be used to effectively solve the problems related to velocity ambiguity and Doppler-angle coupling, meanwhile the accuracy of velocity and angle estimation can be improved. An improved algorithm is tested on the vehicle datasets that are gathered in the forward direction of ordinary public scenes of a city. The experimental results further verify the feasibility of the method, which meets the real-time and accuracy requirements of ITSs on vehicle information monitoring.
ER -