Mobility-Aware Localization in mmWave Channel: Adaptive Hybrid Filtering Approach

Publication Type

Journal Article

Name of Author

• Abidemi Orimogunje, African Center of Excellence in Internet of Things (ACEIoT) University of Rwanda Kigali, RwandaFollow
• Kyeong-Ju Cha, Department of Electronic Engineering, Hanyang University
• Hyunwoo Park, Department of Electronic Engineering, Hanyang University
• Abdulahi Abiodun Badrudeen, Department of Electronic Engineering Hanyang University Seoul, South Korea
• Sunwoo Kim, Department of Electronic Engineering Hanyang University Seoul, South Korea
• Dejan Vukobratovic, Department of of Power Electronics and Communication Engineering University of Novi Sad, Serbia

Publication Date (Issue Year)

2025

Journal Name

IEEE Xplore

Abstract

Precise user localization and tracking enhance energy-efficient and ultra-reliable low-latency applications in the next-generation wireless networks. In addition to computational complexity and data association challenges with Kalman-filterlocalization techniques, estimation errors tend to grow as the user’s trajectory speed increases. By exploiting mmWave signals for joint sensing and communication, our approach dispenses with additional sensors adopted in most techniques while retaining high-resolution spatial cues. We present a hybrid mobilityaware adaptive framework that selects between the Extended Kalman Filter at pedestrian speed and the Unscented Kalman Filter at vehicular speeds. The scheme mitigates data-association problem and estimation errors through adaptive noise scaling, chi-square (χ2) gating, and Rauch-Tung-Striebel smoothing. Evaluations using Absolute Trajectory Error, Relative Pose Error, Normalized Estimated Error Squared, and Root Mean Square Error metrics demonstrate roughly 30-60 % improvement in their respective regimes, indicating a clear advantage over existing approaches tailored to either indoor or static settings.

Keywords

Mobility-Aware Localization, mmWave Channel, Adaptive Hybrid

Rsif Scholar Name

Abidemi Matthew Orimogunje

Rsif Scholar Nationality

Nigeria

Cohort

Cohort 4

Thematic Area

ICTs Including Big Data and Artificial Intelligence

Africa Host University (AHU)

University of Rwanda (UR), Rwanda

Recommended Citation

Orimogunje, A., Cha, K., Park, H., Badrudeen, A. A., Kim, S., & Vukobratovic, D. (2025). Mobility-Aware Localization in mmWave Channel: Adaptive Hybrid Filtering Approach. IEEE Xplore https://doi.org/10.1109/VCC67261.2025.11351253