RIM-Net: A Real-Imaginary-Magnitude Network for NLOS/LOS Identification in UWB Indoor Positioning Systems

In UWB-based indoor positioning research, NLOS signals severely degrade localization accuracy. To address the NLOS/LOS disRIMination challenge, this paper proposes the RIM-Net model, which enhances CIR signal feature learning by integrating residual blocks and LSTM architectures. Unlike conventional methods that process only the magnitude of CIR signals, RIM-Net explicitly models both real and imaginary components of CIR signals to improve classification accuracy. Experimental results demonstrate that RIM-Net achieves an average accuracy of 92.36% on benchmark datasets while maintaining generalization capabilities above 85% in unseen environments. Furthermore, the optimization of the TOA algorithm validated the effectiveness of RIM-Net in localization, achieving an average performance improvement of 9.66%. This approach provides an effective solution for NLOS/LOS identification in complex channel conditions within UWB positioning systems.