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

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.