A Novel CNN Model for NLoS Classification in UWB Indoor Positioning System

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Ghaemifar Mohammadali; Ebadollahi Saeed; Ghasemzadeh Milad; Pirahmadian Saba

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2024 | Issue:10

Download Full-Text

View:

Download:

Information Seminar Paper

Title

A Novel CNN Model for NLoS Classification in UWB Indoor Positioning System

Author(s)

Ghaemifar Mohammadali | Ebadollahi Saeed | Ghasemzadeh Milad | Pirahmadian Saba

Keywords

Internet of Things

Indoor Positioning Systems

UWB

NLoS Classification

Deep Learning

CNN

Abstract

According to the research conducted, people spend about 70-90% of their living and working time indoors. Therefore, providing systems that offer adequate services to users in these environments seems essential. Locating users and devices is widely used in healthcare, industry, building management, surveillance, and other areas. There are various technologies for Indoor Positioning Systems. In this paper, Ultra Wide Band (UWB) technology is considered due to its high accuracy in indoor positioning. However, there are many objects and people in indoor environments, so obstacles can reflect the transmitted signals. Compared to the Line of Sight (LoS) signal, the delay of the signal transmission path in the Non-Line of Sight (NLoS) signal leads to positive range errors. In order to reduce the effect of NLoS conditions on positioning. In this research, we have attempted to achieve high-precision accuracy separation for LoS and NLoS conditions by providing Deep Learning networks and using channel impulse response data as input without prior knowledge of the environment. In addition, the result of this classification is compared to other references that used a similar dataset. The results of the NLoS/LoS signal classification section show that the proposed Convolutional Neural Networks (CNN) are better than other neural network methods (such as Deep neural networks).

Cites

No record.

References

No record.

Cite

APA: Copy

Ghaemifar, Mohammadali, Ebadollahi, Saeed, Ghasemzadeh, Milad, & Pirahmadian, Saba. (2024). A Novel CNN Model for NLoS Classification in UWB Indoor Positioning System. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/1147669/en

Vancouver: Copy

Ghaemifar Mohammadali, Ebadollahi Saeed, Ghasemzadeh Milad, Pirahmadian Saba. A Novel CNN Model for NLoS Classification in UWB Indoor Positioning System. 2024. Available from: https://sid.ir/paper/1147669/en

IEEE: Copy

Mohammadali Ghaemifar, Saeed Ebadollahi, Milad Ghasemzadeh, and Saba Pirahmadian, “A Novel CNN Model for NLoS Classification in UWB Indoor Positioning System,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2024, [Online]. Available: https://sid.ir/paper/1147669/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops