ML-based LOS/NLOS/multipath signal classifiers for GNSS in simulated multipath environment

Koiloth, S. R. S. Jyothsna; Achanta, Dattatreya Sarma; Koppireddi, Padma Raju

doi:10.1007/s42401-023-00255-0

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Notably, non-line-of-sight (NLOS) conditions can degrade positioning accuracy substantially due to signal obstruction, with errors up to several meters [10,11]. Multipath propagation, due to NLOS signals in combination with the original LOS signal [12], introduces errors ranging from a few centimeters to over a meter [13], depending on environmental complexity [14]. Multipath error is more common but much easier to ignore, more difficult to avoid, and significantly impacts positioning accuracy.…”

Section: Introductionmentioning

confidence: 99%

System and Method for Reducing NLOS Errors in UWB Indoor Positioning

Wang,

Zhang,

et al. 2024

Applied Sciences

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The ultra-wideband (UWB) technology has been increasingly recognized as an efficacious strategy for Indoor Positioning Systems (IPSs). However, the accuracy of the UWB system can be severely degraded by non-line-of-sight (NLOS) errors. In this study, we proposed a new method to reduce the UWB positioning error in such an indoor environment. We developed a system consisting of a Robotic Total Station (RTS), four UWB base stations, a moving target (including a prism and a UWB tag), and a PC. The observed coordinates of the moving target, captured using millimeter precision from an RTS device, served as the ground truth for calculating the positioning errors of the UWB tag. In a significant NLOS scenario, the UWB’s three-dimensional positioning error was identified to exceed the nominal value declared by the manufacturer by a factor of more than three. A detailed analysis revealed that each coordinate component’s error distribution pattern demonstrated considerable variance. To reduce the NLOS error, we designed a combined multilayer neural network that simultaneously fits errors on all three coordinate components and three separate multilayer networks, each dedicated to optimizing errors on a single coordinate component. All networks were trained and verified by benchmark errors obtained from the RTS. The results showed that neural networks outperform the traditional methods, attributed to their strong nonlinear modelling ability, thereby significantly improving the external accuracy by an average reduction in RMSE by 61% and 72%. It is evident that the proposed separate networks would be more suitable for NLOS positioning problems than a combined network.

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Section: Introductionmentioning

confidence: 99%

System and Method for Reducing NLOS Errors in UWB Indoor Positioning

Wang,

Zhang,

et al. 2024

Applied Sciences

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A novel method for anomaly detection and correction of GNSS time series

Li,

Xie,

Meng

et al. 2024

Meas. Sci. Technol.

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Global Navigation Satellite Systems (GNSS) provides a novel means for deformation monitoring, which is an important guarantee for structures. Accurately separating its linear and nonlinear signals, and noise in GNSS time series is the foundation for analyzing deformation mechanisms and the prerequisite for assessing the status. However, extracting deformation signals is a challenging obstacle to applying GNSS for deformation monitoring. Aimed at that, a new method is proposed in this study. Fine-tuning the window size and threshold of the Hampel filter through grid search allows for initial anomaly detection and correction. Optimizing the K parameter of the K-Nearest Neighbors (KNN) algorithm via rigorous K-Fold Cross-Validation ensures further smoothing of the filtered data, which overcomes the limitations of the Hampel filter in handling continuous anomalies. Experimental results demonstrate that the proposed method improves performance by approximately 62% compared to traditional methods and by around 40% relative to IQR and other methods. This study presents an effective approach for detecting and eliminating outliers in GNSS deformation monitoring, offering noteworthy theoretical and practical implications.

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ML-based LOS/NLOS/multipath signal classifiers for GNSS in simulated multipath environment

Cited by 2 publications

References 19 publications

System and Method for Reducing NLOS Errors in UWB Indoor Positioning

System and Method for Reducing NLOS Errors in UWB Indoor Positioning

A novel method for anomaly detection and correction of GNSS time series

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