Indoor and Outdoor Localization Technologies
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Experimental results demonstrate that TADA achieves a 44.7% improvement in RMSE compared to conventional EKF and significantly outperforms the state-of-the-art GMC-EKF algorithm, which provides a robust and high-precision solution for indoor positioning.
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Abstract Ultra-wideband (UWB) 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.
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@article{Shi2026Closed,
title = {Closed-loop collaborative adaptive architecture for robust UWB positioning in complex indoor environments},
author = {Zhiran Shi and Le Gao and R Chen and Xueyuan Hao and Xianyang Zeng and Q Zhang},
journal = {Measurement Science and Technology},
year = {2026},
doi = {10.1088/1361-6501/ae8bc2},
url = {https://doi.org/10.1088/1361-6501/ae8bc2}
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