Journal
REMOTE SENSING
Volume 14, Issue 19, Pages -Publisher
MDPI
DOI: 10.3390/rs14194902
Keywords
ultra-wideband (UWB); localization; Kalman filter (KF); error modelling; Internet of Things (IoT)
Categories
Funding
- National Natural Science Foundation of China [12102203, 61901235]
- Natural Science Foundation of Jiangsu Province [BK20200971]
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This paper presents a novel error modelling approach based on the curve fitted Kalman filter algorithm to improve the accuracy and precision of ultra-wideband (UWB) localization.
At present, the ultra-wideband (UWB) technology plays a vital role in the environment of indoor localization. As a new technology of wireless communications, UWB has many advantages, such as high accuracy, strong anti-multipath ability, and high transmission rate. However, in real-time operation, the accuracy of UWB is reduced by multi-sensor interference, antenna variations and system operation noise. We have developed a novel error modelling based on the curve fitted Kalman filter (CFKF) algorithm to solve these issues. This paper involves investigating and developing the error modelling algorithm that can calibrate the signal sensors, reduce the errors, and mitigate noise levels and interference signals. As part of the research investigation, a range of experiments was executed to validate the CFKF error modelling approach's accuracy, reliability and viability. The experimental results indicate that this novel approach significantly improves the accuracy and precision of beacon-based localization. Validation tests also show that the CFKF error modelling method can improve the localization accuracy of UWB-based solutions.
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