[2023 - 2026]

@article{Hu2025c,

title = {DoA Estimation and Kalman Filter based Multi-Antenna System for Vehicle Position in mmWave Network},

author = {Dou Hu and Jin Nakazato and Javanmardi Ehsan and Kazuki Maruta and Rui Dinis and Manabu Tsukada},

doi = {10.1109/OJVT.2025.3608747},

issn = {2644-1330},

year  = {2025},

date = {2025-09-14},

urldate = {2025-09-14},

journal = {IEEE Open Journal of Vehicular Technology},

volume = {6},

pages = {2761-2775},

abstract = {With the advancement of Beyond 5G/6G technologies, accurate positioning and velocity estimation in Internet of Vehicles (IoV) systems has become increasingly critical. Although GPS can provide real-time location information, its performance degrades significantly in environments with heavy obstructions, such as urban areas surrounded by skyscrapers. To address this limitation, this study proposes a positioning framework that relies on channel parameter estimation derived from multi-antenna signal processing. Specifically, we adopt an adaptive low-complexity 2D MUSIC (ALC2D-MUSIC) algorithm to estimate signal directions, and further apply an unscented Kalman filter (UKF) using extracted Direction of Arrival (DoA) and Time of Arrival (ToA) information to estimate vehicle positions and velocities. The proposed system is robust to variations in road geometry, making it suitable for deployment in diverse traffic environments. Simulation results demonstrate that our method achieves high estimation accuracy and outperforms a compressive sensing-based approach across different SNR levels, angular search resolutions, and antenna array sizes. Furthermore, the UKF-based tracking algorithm shows superior performance in curved road scenarios, validating its effectiveness under realistic mobility conditions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hu2025b,

title = {OTFS Based RIS-assisted vehicle positioning and tracking in V2X scenario},

author = {Dou Hu and Jin Nakazato and Kazuki Maruta and Rui Dinis and Omid Abbassi Aghda and and Manabu Tsukada},

doi = {10.1109/TCOMM.2025.3588559},

issn = {0090-6778},

year  = {2025},

date = {2025-07-08},

urldate = {2025-07-08},

journal = {IEEE Transactions on Communications},

volume = {73},

issue = {11},

pages = {10676-10691},

abstract = {This paper proposes a novel and robust framework for velocity and position estimation in vehicle-to-everything (V2X) communication networks, targeting challenges that arise when traditional GPS systems and line-of-sight (LoS) channels fail due to signal blockages between vehicles, base stations (BSs), and satellites. The proposed solution integrates multiple complementary methods to enhance robustness and accuracy. Specifically, the framework first applies the MUSIC algorithm for angle-of-arrival (AoA) estimation to obtain reliable initial positioning. In parallel, a newly designed low peak-to-average power ratio (PAPR) frame structure is introduced under reconfigurable intelligent surface (RIS)-assisted Orthogonal Time Frequency Space (OTFS) modulation, enabling resilient velocity embedding against noise and interference. These methods jointly support an unscented Kalman Filter (UKF), which refines the velocity and position estimation with high reliability. Extensive numerical evaluations confirm the effectiveness of the proposed multi-method integrated framework. Various modulation techniques, including zero padding (ZP), cyclic prefix (CP), and their recursive variants (e.g., RZP, RCP), demonstrate improved bit error rate (BER) performance over traditional OFDM. The proposed low-PAPR signal design achieves a 21.5% PAPR reduction compared to conventional embedded pilot schemes, significantly improving BER in 4-QAM scenarios. Velocity estimation results also show that the UKF outperforms the extended Kalman Filter (EKF) in both straight and curved road conditions. By combining MUSIC-based AoA estimation with RIS assistance under NLoS conditions, the proposed framework remains effective even in high-Doppler and low-SNR environments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}