Closed-loop collaborative adaptive architecture for robust UWB positioning in complex indoor environments
TL;DR
Experimental results demonstrate that TADA achieves a 44.7% improvement in RMSE compared to conventional EKF and significantly outperforms the state-of-the-art (SOTA) GMC-EKF algorithm and provides a robust and high-precision solution for indoor positioning.
Abstract
Ultra-wideband positioning accuracy in indoor spaces is often limited by the combined effects of path loss and transient non-line-of-sight (NLOS) interference. While pre-calibration can mitigate base errors, it remains ineffective against dynamic environmental changes and random occlusions. To bridge this gap, this paper proposes a two-stage adaptive disturbance-aware architecture (TADA). The framework integrates a signal-layer adaptive path-loss compensation module with dynamic forgetting factor (APLC-DFF) and a state-layer NLOS-resilient composite filter (NRCF). By utilizing a cross-layer residual feedback mechanism, TADA enables multi-timescale error separation: APLC-DFF adaptively self-calibrates slow-varying systematic biases, while NRCF actively compensates for transient NLOS disturbances via a disturbance observer. Experimental results demonstrate that TADA achieves a 44.7% improvement in RMSE compared to conventional EKF and significantly outperforms the state-of-the-art (SOTA) GMC-EKF algorithm. This paradigm provides a robust and high-precision solution for indoor positioning.