Ammar Fahem Majeed1,2,*, Rashidah Arsat1, Muhammad Ariff Baharudin1, Nurul Mu’azzah Abdul Latiff1, Abbas Albaidhani3
Computer Systems Science and Engineering, Vol.49, pp. 159-183, 2025, DOI:10.32604/csse.2024.058741
- 03 January 2025
Abstract Accurate non-line of sight (NLOS) identification technique in ultra-wideband (UWB) location-based services is critical for applications like drone communication and autonomous navigation. However, current methods using binary classification (LOS/NLOS) oversimplify real-world complexities, with limited generalisation and adaptability to varying indoor environments, thereby reducing the accuracy of positioning. This study proposes an extreme gradient boosting (XGBoost) model to identify multi-class NLOS conditions. We optimise the model using grid search and genetic algorithms. Initially, the grid search approach is used to identify the most favourable values for integer hyperparameters. In order to achieve an optimised model configuration,… More >
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