Novel Fine-Tuned Attribute Weighted Na\"ive Bayes NLoS Classifier for UWB Positioning. (arXiv:2304.11067v1 [eess.SP])

In this paper, we propose a novel Fine-Tuned attribute Weighted Na\"ive Bayes
(FT-WNB) classifier to identify the Line-of-Sight (LoS) and Non-Line-of-Sight
(NLoS) for UltraWide Bandwidth (UWB) signals in an Indoor Positioning System
(IPS). The FT-WNB classifier assigns each signal feature a specific weight and
fine-tunes its probabilities to address the mismatch between the predicted and
actual class. The performance of the FT-WNB classifier is compared with the
state-of-the-art Machine Learning (ML) classifiers such as minimum Redundancy
Maximum Relevance (mRMR)- $k$-Nearest …

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