A Hybrid Deep Learning Framework for Precision UWB Ranging Measurement and Real-Time NLOS Error Mitigation on Edge Instruments

Abstract

Ultrawideband (UWB) technology is critical for precise distance measurement in complex environments; however, its accuracy is severely degraded under non-line-of-sight (NLOS) propagation. In particular, human-body shadowing introduces multipath distortion and signal attenuation, resulting in significant systematic ranging errors. Existing mitigation methods typically face a tradeoff; they either rely on computationally expensive architectures unsuitable for edge-level measurement devices or use simplified feature sets that fail to capture the temporal channel dynamics required for accurate error characterization. To address this problem, this article proposes a computationally efficient hybrid deep learning framework that integrates a convolutional long short-term memory (CNN–LSTM) network with a multilayer perceptron (MLP). The dual-path architecture jointly exploits raw channel impulse response (CIR) waveforms and static channel features, enabling simultaneous modeling of spatial–temporal dependencies and nonlinear feature relationships. Experimental validation across four diverse measurement environments shows that the proposed floating-point model reduces the ranging root-mean-square error (RMSE) to 0.1186 m, significantly outperforming raw two-way ranging (TWR) and single-modality baselines. Moreover, the model remains compact, with only 17 570 parameters, and after full-integer INT8 quantization, it still achieves an RMSE of 0.1219 m, supporting practical deployment on microcontroller unit (MCU)-class edge platforms. These results demonstrate the potential of the proposed framework as a practical high-precision UWB error-mitigation solution for resource-constrained edge instrumentation and wearable IoT devices.