@article{01KH0VMDGJ7Y808KH2Y3QAK4KE,
  abstract     = {{In the rapidly evolving Internet of Things (IoT) landscape, accurate indoor positioning is increasingly vital. The proposed algorithm synergizes an ultrawideband (UWB) sensor with an inertial measurement unit (IMU) and artificial intelligence to obtain precise positioning in nonline-of-sight (NLOS) scenarios. In the proposed UWB module, a large language model (LLM) such as bidirectional encoder representations from transformers (BERT) algorithm is designed to utilize the channel impulse response (CIR) for effective NLOS identification and UWB ranging trustworthiness evaluation. Concurrently, the IMU module is also designed with BERT to recognize various pedestrian activity states, thereby optimizing positioning. BERT's self-attention mechanism and deep learning (DL) bidirectional training efficiently extract essential features from sequential data, capturing both local and global information. The integration of both UWB and IMU through a proposed tightly coupled algorithm significantly boosts positioning performance. Experimental campaigns demonstrate an average NLOS identification accuracy, line-of-sight (LOS), and F2 of 98.8%, 99.4%, and 0.9926, respectively. These performances surpass the state-of-the-art least-squares support vector machine (LS-SVM), convolutional neural network (CNN), and CNN with long short-term memory (CNN-LSTM) up to 17.66% in NLOS identification. In terms of pedestrian activity recognition using BERT, the BERT algorithm achieves a precision (recall) of 99.3% (99.4%), notably outperforming the CNN and CNN-LSTM by 17.9% (16.2%) and 11.9% (10.9%), respectively. Finally, the UWB-IMU algorithm significantly enhances positioning accuracy by 80.5%, outperforming Kalman, LSTM-EKF, and particle filter (PF) methods by 68.1%, 48.3%, and 45.0%, respectively. The proposed approach presents a robust solution for indoor positioning for IoT applications, particularly in challenging NLOS environments.}},
  articleno    = {{8509417}},
  author       = {{Yang, Hongchao and Wang, Yunjia and Seow, Chee Kiat and Li, Zengke and Sun, Meng and De Cock, Cedric and Bi, Jingxue and Joseph, Wout and Plets, David}},
  issn         = {{0018-9456}},
  journal      = {{IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT}},
  keywords     = {{LOCALIZATION,MITIGATION,SYSTEM,CLASSIFICATION,RECOGNITION,Accuracy,Distance measurement,Feature extraction,Channel impulse response,Encoding,Bidirectional control,Convolutional neural networks,Vectors,Pedestrians,Internet of Things,Channel impulse response (CIR),indoor positioning system (IPS),inertial measurement unit (IMU),large language model (LLM),transformer,ultrawideband (UWB)}},
  language     = {{eng}},
  pages        = {{17}},
  title        = {{NLOS identification and ranging trustworthiness for indoor positioning with LLM-based UWB-IMU fusion}},
  url          = {{http://doi.org/10.1109/TIM.2025.3554900}},
  volume       = {{74}},
  year         = {{2025}},
}

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