WDMA-UWB Indoor Positioning Through Channel Classification-Based NLOS Mitigation Approach

Liu, Qingzhi; Zhao, Yanlong; Yin, Zhendong; Wu, Zhilu

doi:10.1109/jsen.2024.3434329

IEEE Sensors J.

2024

DOI: 10.1109/jsen.2024.3434329

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WDMA-UWB Indoor Positioning Through Channel Classification-Based NLOS Mitigation Approach

Qingzhi Liu,

Yanlong Zhao,

Zhendong Yin

et al.

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2024

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Cited by 3 publications

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Robust Classification of UWB NLOS/LOS Using Combined FCE and XGBoost Algorithms

Wang,

Ahmad

2024

IEEE Access

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Robust Classification of UWB NLOS/LOS Using Combined FCE and XGBoost Algorithms

Wang,

Ahmad

2024

IEEE Access

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A Design of NLOS Communication Scheme Based on SC-FDE with Cyclic Suffix for UAV Payload Communication

Wang,

Xiang,

Wang

et al. 2024

Drones

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Non-line-of-sight (NLOS) communication with severe loss always leads to performance degradation in unmanned aerial vehicle (UAV) payload communication. In this paper, a UAV NLOS communication scheme based on single-carrier frequency domain equalization with cyclic prefix and cyclic suffix (CP/CS-SC-FDE) is designed. First, the reasons behind the generation of later intersymbol interference (LISI) in UAV NLOS communication are investigated. Then, the frame structure of conventional single-carrier frequency domain equalization with cyclic prefix (CP-SC-FDE) is improved, and the UAV NLOS communication frame structure based on cyclic prefix (CP) and cyclic suffix (CS) is designed. Furthermore, a channel estimation algorithm applicable to this scheme is proposed. The numerical results show that this UAV communication scheme can eliminate intersymbol interference (ISI) in NLOS communication. Compared with the conventional CP-SC-FDE system, this scheme can achieve excellent performance in the Rayleigh channel and other standard NLOS channels. In the CP/CS-SC-FDE system, the BER result is similar to that under ideal synchronization.

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Multimodal Image-Based Indoor Localization with Machine Learning—A Systematic Review

Łukasik,

Szott,

Leszczuk

2024

Sensors

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Outdoor positioning has become a ubiquitous technology, leading to the proliferation of many location-based services such as automotive navigation and asset tracking. Meanwhile, indoor positioning is an emerging technology with many potential applications. Researchers are continuously working towards improving its accuracy, and one general approach to achieve this goal includes using machine learning to combine input data from multiple available sources, such as camera imagery. For this active research area, we conduct a systematic literature review and identify around 40 relevant research papers. We analyze contributions describing indoor positioning methods based on multimodal data, which involves combinations of images with motion sensors, radio interfaces, and LiDARs. The conducted survey allows us to draw conclusions regarding the open research areas and outline the potential future evolution of multimodal indoor positioning.

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WDMA-UWB Indoor Positioning Through Channel Classification-Based NLOS Mitigation Approach

Cited by 3 publications

References 34 publications

Robust Classification of UWB NLOS/LOS Using Combined FCE and XGBoost Algorithms

Robust Classification of UWB NLOS/LOS Using Combined FCE and XGBoost Algorithms

A Design of NLOS Communication Scheme Based on SC-FDE with Cyclic Suffix for UAV Payload Communication

Multimodal Image-Based Indoor Localization with Machine Learning—A Systematic Review

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