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

|View full text |Cite

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

Qingzhi Liu,

Yanlong Zhao,

Zhendong Yin

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

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

Łukasik,

Szott,

Leszczuk

2024

Sensors

View full text Add to dashboard Cite

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.

show abstract

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

Łukasik,

Szott,

Leszczuk

2024

Sensors

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

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

Cited by 1 publication

References 34 publications

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

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

Contact Info

Product

Resources

About