Oct 2026· IEEE Sensors Journal· Vol 26, pp. 31039-31047· 0 citations· 28 references
Abstract
The ultrawideband (UWB) sensors are widely used for mobile robot localization in indoor and GNSS-denied environments. However, practical UWB measurements are often affected by nonline-of-sight propagation, multipath reflections, and intermittent outliers, which may introduce non-Gaussian residuals and degrade sensing reliability. To improve robustness under unreliable UWB measurements, this article proposes a Gaussian mixture model (GMM)-guided neural moving horizon estimation (MHE) framework for mobile robot localization. The GMM is used to model the residual distribution and estimate the posterior outlier probability of each UWB residual. A confidence factor is then constructed to attenuate unreliable measurement information in the MHE cost. Meanwhile, a lightweight neural network is introduced to adaptively adjust the weighting matrices in the MHE objective. In this way, probabilistic UWB reliability evaluation and adaptive MHE weighting are integrated within a unified estimation framework. The real-world LOS and NLOS experiments demonstrate that GMM-NNMHE improves localization accuracy and suppresses large positioning errors compared with filtering, fixed-weight MHE, neural MHE, and robust factor-graph baselines.
The ultrawideband (UWB) technology has been proven to achieve continuous absolute positioning for wheeled robots. However, its accuracy is often degraded by non-line-of-sight (NLOS) propagation in complex environments. In this article, a channel impulse response (CIR)-based method is proposed to improve the NLOS outlie...
Xiao-Ji Niu, Shuang-Yan Liu, Zi-Hang Zhang et al.· IEEE Transactions on Instrum...· 0 citations
The proposed Inconsistency-Aware Robust Trilateration (IART), a lightweight framework that predicts per-anchor inconsistency and uses it to adapt measurement uncertainty within an Iteratively Reweighted Least Squares Gauss–Newton (IRLS-GN) solver, suppresses unreliable range measurements without temporal filtering or I...
Ren Fan, Hyuckjin Choi, Yutaka Arakawa· IEEE Journal of Indoor and S...· 0 citations
Herein, the distributed variational Bayesian-assisted unscented Kalman filter (VB-UKF) is used to enhance the accuracy of pedestrian positioning. For this method, a distributed filter is employed. First, the UKF under colored measurement noise (CMN) is derived. The VB-assisted method is then derived. Subsequently, the...
Mao-Xiang Zhou, Hao-Ran Yin, Huan-Kun Liu et al.· Electronics· 0 citations
Indoor robot positioning plays an important role in ensuring autonomous navigation in smart environments where Global Positioning System (GPS) signals are unavailable. Ultra-Wideband (UWB) technology offers high accuracy and multipath interference resistance, providing a suitable foundation for indoor positioning syste...
Duc Minh Dang, Phong Tan Nguyen, Quang Viet Pham et al.· REV Journal on Electronics a...· 0 citations
To address the degradation in accuracy and reliability of ultra-wideband (UWB)-based indoor localization for automated guided vehicles (AGVs) under non-line-of-sight (NLOS) conditions, this paper proposes an MCKF-enhanced Chan-Taylor fusion localization algorithm. First, the double-sided two-way ranging (DS-TWR) method...
Fan-Chao Zhao, Shou-Xue Liao, Guang-Lin Liu et al.· Measurement and control (Lon...· 0 citations
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