Mobility-Aware Localization in mmWave Channel: Adaptive Hybrid Filtering Approach
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912634978172928 |
|---|---|
| author | Orimogunje, Abidemi Cha, Kyeong-Ju Park, Hyunwoo Badrudeen, Abdulahi A. Kim, Sunwoo Vukobratovic, Dejan |
| author_facet | Orimogunje, Abidemi Cha, Kyeong-Ju Park, Hyunwoo Badrudeen, Abdulahi A. Kim, Sunwoo Vukobratovic, Dejan |
| contents | Precise user localization and tracking enhances 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-filter localization 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 mobility-aware adaptive framework that selects the Extended Kalman filter at pedestrian speed and the Unscented Kalman filter at vehicular speed. The scheme mitigates data-association problem and estimation errors through adaptive noise scaling, chi-square gating, 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06861 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mobility-Aware Localization in mmWave Channel: Adaptive Hybrid Filtering Approach Orimogunje, Abidemi Cha, Kyeong-Ju Park, Hyunwoo Badrudeen, Abdulahi A. Kim, Sunwoo Vukobratovic, Dejan Signal Processing Precise user localization and tracking enhances 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-filter localization 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 mobility-aware adaptive framework that selects the Extended Kalman filter at pedestrian speed and the Unscented Kalman filter at vehicular speed. The scheme mitigates data-association problem and estimation errors through adaptive noise scaling, chi-square gating, 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. |
| title | Mobility-Aware Localization in mmWave Channel: Adaptive Hybrid Filtering Approach |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2510.06861 |