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

arXiv: https://arxiv.org/abs/2304.11067 · HTML: https://mlsanity.com/paper/2304.11067/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 14 Apr 2023.

## Builds on (most cited first, 1 of 1)

- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)

## Built on by (most cited first, 1 of 1)

- [Bluetooth Low Energy Dataset Using In-Phase and Quadrature Samples for Indoor Localization](https://mlsanity.com/paper/2412.01767/) (2024)

## Abstract (arXiv)

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.