millimeter wave (mmWave) radar; millimeter wave identification (MMID); mutation particle filter (MPF); Radio frequency identification (RFID); Global positioning; Millimeter wave identification; Millimeter-wave identifications; Millimeter-wave radar; Millimetre-wave radar; Mutation particle filter; Particle filter; Radio frequency identification; Radio-frequency-identification; Vehicle localization; Automotive Engineering; Radar; Millimeter wave communication; Location awareness; Radar antennas; Frequency modulation; Radiofrequency identification; Backscatter; Integrated sensing and communication; Transmitting antennas; Bandwidth
Abstract :
[en] Millimeter wave (mmWave) radar has become a widely adopted technology in vehicles and advanced driver assistance systems (ADAS). Meanwhile, as wireless communication progresses to higher frequencies, the role of the mmWave band has expanded, now supporting both sensing and 5G communication, which has given rise to integrated sensing and communication (ISAC). In this paper, we propose a novel mmWave ISAC vehicle localization system, which innovatively integrates emerging mmWave identification (MMID) technology into conventional automotive mmWave radar, enabling automotive radar to interact with roadside MMID tags, thereby achieving lane-level vehicle localization independent of the global positioning system (GPS). Unlike existing RFID-based vehicle localization solutions, the proposed solution is more practical for real-world deployment, as it eliminates the need to install additional large RFID antennas on vehicles. To achieve this, we first analyze the backscatter modulation characteristics of MMID tags and propose a novel frequency modulation strategy that lays the foundation for distinguishing signals from different tags within radar echoes that contain various tags and other objects. Subsequently, based on the distance, relative velocity, and azimuth of the tags, we perform static parameter estimation of the vehicle using the least squares (LS) algorithm. Finally, we construct a vehicle motion model and introduce a novel mutation particle filter (MPF) algorithm to estimate the dynamic motion state of the vehicle, ultimately achieving precise vehicle position tracking. The proposed system offers a practical solution for GPS-denied vehicle localization, aligning with the future vision of 6G-enabled intelligent transportation systems (ITS) and IoT-driven smart cities.
Long, Wen-Xuan ; University of Pisa, Dipartimento di Ingegneria dell'Informazione, Pisa, Italy
Song, Wenfei; State Key Laboratory of Integrated Services Networks (ISN), Xidian University, China ; Xidian University, Guangzhou Institute of Technology, Guangzhou, China
LIU, Yuan ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SPARC
Liu, Yongjun; State Key Laboratory of Integrated Services Networks (ISN), Xidian University, China ; Xidian University, Guangzhou Institute of Technology, Guangzhou, China
Moretti, Marco ; University of Pisa, Dipartimento di Ingegneria dell'Informazione, Pisa, Italy ; National Inter-University Consortium for Telecommunications (CNIT), Parma, Italy
Chen, Rui ; State Key Laboratory of Integrated Services Networks (ISN), Xidian University, China ; Xidian University, Guangzhou Institute of Technology, Guangzhou, China
External co-authors :
yes
Language :
English
Title :
GPS-Denied ISAC Vehicle Localization Based on mmWave Radar and Identification
Publication date :
05 August 2025
Journal title :
IEEE Open Journal of Vehicular Technology
eISSN :
2644-1330
Publisher :
Institute of Electrical and Electronics Engineers Inc.
FNR15407066 - MASTERS - Modelling And Simulation Of Complex Radar Scennes, 2020 (01/07/2021-30/06/2024) - Bhavani Shankar Mysore Rama Rao
Funders :
Italian Ministry of Education and Research National Natural Science Foundation of China Guangdong Natural Science Fund for Distinguished Young Scholar Luxembourg National Research Fund
Funding number :
BRIDGES2020/IS/15407066
Funding text :
The work of Wen-Xuan Long and Marco Moretti was supported in part by the Italian Ministry of Education and Research (MUR) through the Framework of the FoReLab Project (Departments of Excellence) and in part by Project GARDEN funded by EU in NextGenerationEU Plan through Italian \u201CBando Prin 2022-D.D.1409 del 14-09-2022. The work of Wenfei Song, Yongjun Liu, and Rui Chen was supported in part by the National Natural Science Foundation of China under Grant 62271376, in part by the Guangdong Natural Science Fund for Distinguished Young Scholar under Grant 2023B1515020079. The work of Yuan Liu was supported by the Luxembourg National Research Fund (FNR) through BRIDGES Project MASTERS under grant BRIDGES2020/IS/15407066.
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