Artificial intelligence for localisation of ultra-wide bandwidth (UWB) sensor nodes

Fuhu Che, Abbas Ahmed, Qasim Ahmed, M. Z. Shakir

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


In this chapter, we have designed an NB classifier for a UWB-based localization system. With the help of NB classifier and RMSE, the data are classified into three categories: high, medium, and low accuracy. ROCs are plotted to show the effec-tiveness of the NB classifier. As our developed technique obtains more than 90% classification accuracy, we have tested it into two different environments: LOS and partial NLOS conditions. Furthermore, to test the accuracy, small-sized and medium-sized rooms were used. From our measurements, it is observed that the accuracy of the developed NB classifier is dependent upon the environment. For LOS and NLOS envi-ronments, the accuracy are around 97% and 87.38%, respectively. Our future research will concentrate on technique that can further improve the localization classification and improve the positioning accuracy of the IPS
Original languageEnglish
Title of host publicationAI for Emerging Verticals
Subtitle of host publicationHuman-Robot Computing, Sensing and Networking
EditorsMuhammad Zeeshan Shakir, Naeem Ramzan
Number of pages15
ISBN (Electronic)9781785619830
ISBN (Print)9781785619823
Publication statusPublished - 31 Dec 2020

Publication series

NameComputing and Networks


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