Novel Fine-Tuned Attribute Weighted Na\"ive Bayes NLoS Classifier for UWB Positioning

• URL: http://arxiv.org/abs/2304.11067v1
• Date: Fri, 14 Apr 2023 09:59:18 GMT
• Title: Novel Fine-Tuned Attribute Weighted Na\"ive Bayes NLoS Classifier for UWB Positioning
• Authors: Fuhu Che, Qasim Zeeshan Ahmed, Fahd Ahmed Khan, and Faheem A. Khan
• Abstract summary: We propose a novel Fine-Tuned attribute Weighted Na"ive Bayes (FT-WNB) to identify the Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) 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.
• Score: 0.6299766708197881
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 Neighbour (KNN), Support Vector Machine (SVM), Decision Tree (DT), Na\"ive Bayes (NB), and Neural Network (NN). It is demonstrated that the proposed classifier outperforms other algorithms by achieving a high NLoS classification accuracy of $99.7\%$ with imbalanced data and $99.8\%$ with balanced data. The experimental results indicate that our proposed FT-WNB classifier significantly outperforms the existing state-of-the-art ML methods for LoS and NLoS signals in IPS in the considered scenario.

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