Research on Inertial Navigation and Environmental Correction Indoor Ultra-Wideband Ranging and Positioning Methods

Han, Chunhua; Xue, Shunbiao; Long, Li; Xiao, Xiongquan

doi:10.3390/s24010261

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…Third, methods for improving positioning accuracy by integrating additional positioning signal information have been proposed. Reference [ 31 ] proposed a method to improve indoor positioning accuracy by combining inertial navigation with environmental adjustments. This involves digitizing spatial settings, estimating coordinates through inertial navigation, and iteratively refining positioning for increased precision.…”

Section: Related Workmentioning

confidence: 99%

5G Indoor Positioning Error Correction Based on 5G-PECNN

Yang,

Zhang,

et al. 2024

Sensors

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With the development of the mobile network communication industry, 5G has been widely used in the consumer market, and the application of 5G technology for indoor positioning has emerged. Like most indoor positioning techniques, the propagation of 5G signals in indoor spaces is affected by noise, multipath propagation interference, installation errors, and other factors, leading to errors in 5G indoor positioning. This paper aims to address these issues by first constructing a 5G indoor positioning dataset and analyzing the characteristics of 5G positioning errors. Subsequently, we propose a 5G Positioning Error Correction Neural Network (5G-PECNN) based on neural networks. This network employs a multi-level fusion network structure designed to adapt to the error characteristics of 5G through adaptive gradient descent. Experimental validation demonstrates that the algorithm proposed in this paper achieves superior error correction within the error region, significantly outperforming traditional neural networks.

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Section: Related Workmentioning

confidence: 99%

5G Indoor Positioning Error Correction Based on 5G-PECNN

Yang,

Zhang,

et al. 2024

Sensors

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Invariant Feature Matching in Spacecraft Rendezvous and Docking Optical Imaging Based on Deep Learning

Guo,

Wu,

Weng

et al. 2024

Remote Sensing

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In spacecraft rendezvous and docking, traditional methods that rely on inertial navigation and sensor data face challenges due to sensor inaccuracies, noise, and a lack of multi-approach assurance. Focusing on exploring a new approach as assistance, this study marks the first application of deep learning-based image feature matching in spacecraft docking tasks, introducing the Class-Tuned Invariant Feature Transformer (CtIFT) algorithm. CtIFT incorporates an improved cross-attention mechanism and a custom-designed feature classification module. By using symmetric multi-layer cross-attention, it gradually strengthens inter-feature relationships perception. And, in the feature matcher, it employs feature classification to reduce computational load, thereby achieving high-precision matching. The model is trained on multi-source datasets to enhance its adaptability in complex environments. The method demonstrates outstanding performance across experiments on four spacecraft docking video scenes, with CtIFT being the only feasible solution compared to SIFT and eight state-of-the-art network methods: D2-Net, SuperPoint, SuperGlue, LightGlue, ALIKED, LoFTR, ASpanFormer, and TopicFM+. The number of successfully matched feature points per frame consistently reaches the hundreds, the successful rate remains 100%, and the average processing time is maintained below 0.18 s per frame, an overall performance which far exceeds other methods. The results indicate that this approach achieves strong matching accuracy and robustness in optical docking imaging, supports real-time processing, and provides new technical support for assistance of spacecraft rendezvous and docking tasks.

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Research on Inertial Navigation and Environmental Correction Indoor Ultra-Wideband Ranging and Positioning Methods

Cited by 2 publications

References 25 publications

5G Indoor Positioning Error Correction Based on 5G-PECNN

5G Indoor Positioning Error Correction Based on 5G-PECNN

Invariant Feature Matching in Spacecraft Rendezvous and Docking Optical Imaging Based on Deep Learning

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