A lightweight CIR-based CNN with MLP for NLOS/LOS identification in a UWB positioning system

Si, M., Wang, Y., Siljak, H., Seow, C. and Yang, H. (2023) A lightweight CIR-based CNN with MLP for NLOS/LOS identification in a UWB positioning system. IEEE Communications Letters, 27(5), pp. 1332-1336. (doi: 10.1109/LCOMM.2023.3260953)

Abstract

Implementing line-of-sight (LOS) and none-line-of-sight (NLOS) identification in ultra-wideband (UWB) systems is crucial. Convolutional neural network (CNN) based identification methods can extract higher-level features automatically, but they are based on channel impulse response (CIR)-turned image ingested features that impose calculation complexity and do not make use of manual features due to the data inundation risk. In this letter, we propose a novel multilayer perceptron (MLP)-based LOS/NLOS identification algorithm that can utilize both manually extracted features and feature from CNN based on raw CIR inputs with only 7.39% calculation complexity as compared to the traditional image-based CNN. Three experiments, at a teaching building, an office, and an underground mine, were conducted to verify the proposed method’s performance. Our proposed features are conducive to LOS/NLOS identification, especially the proposed raw CIR-based feature from the CNN, achieving 26.9% improvement over existing manual features. Furthermore, the proposed method outperformed the traditional image-based CNN with an improvement of 44.16%.

• Item Type: Articles
• Keywords: UWB, LOS/NLOS identification, CNN, MLP.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Seow, Dr Chee Kiat
• Authors: Si, M., Wang, Y., Siljak, H., Seow, C., and Yang, H.
• Subjects: Q Science > Q Science (General); Q Science > QA Mathematics; Q Science > QA Mathematics > QA75 Electronic computers. Computer science; T Technology > T Technology (General); T Technology > TA Engineering (General). Civil engineering (General)
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: IEEE Communications Letters
• Publisher: IEEE
• ISSN: 1089-7798
• ISSN (Online): 1558-2558
• Published Online: 23 March 2023
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE Communications Letters 27(5): 1332-1336
• Publisher Policy: Reproduced in accordance with the publisher copyright policy