Novel Fine-Tuned Attribute Weighted Naïve Bayes NLoS Classifier for UWB Positioning

Abstract

In this paper, we propose a novel Fine-Tuned attribute Weighted Naïve 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), Naïve 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.

Indoor Positioning System (IPS) Using Ultra-Wide Bandwidth (UWB)—For Industrial Internet of Things (IIoT)

“…Generally, in a UWB IPS, the signals are classified as either a LoS or NLoS signal [ 27 , 78 , 79 , 80 , 81 ]. There are some papers where the signals are classified as having quasiLoS (QLoS); see [ 82 , 83 ] and references therein.…”

“…This sub-section presents the results obtained by applying ML-based algorithms, such as KNN-, SVM-, DT-, NB-, and NN-based UWB signal features. For this experiment, 1000 LoS and 100 NLoS UWB signals are used [ 17 , 78 ]. The performance is compared with the running time, confusion matrix, and the correct rate (CR) for LoS and NLoS components.…”

“…In situations where there is an imbalance in the dataset, existing ML algorithms face difficulties in training a robust classifier to classify the NLoS signal. To solve this problem, it is important to develop standard ML methods for training and predicting data that are independent, such as GD, GGD, and WNB algorithms [ 17 , 78 , 79 ].…”

A Study Of Civil Engineering Education At Singapore Nanyang Technology University And At The University Of Florida

“…the operation, one type-2 device is removed, or the microsegmentation needs to upgrade the service type to {type-1, type-2, type-3, type-4}, we should involve/remove certain nodes (and the corresponding edges) while retaining the overall topology. Hence, we leverage the fine-tuning technique [46] to align the pre-trained model with new objectives. The fine-tuning process of LEGD can be expressed as…”