UWB Indoor Localization Based on Artificial Rabbit Optimization Algorithm and BP Neural Network

• Published in: Biomimetics (Basel, Switzerland) Vol. 10; no. 6; p. 367
• Main Authors: Jia, Chaochuan, Tao, Can, Yang, Ting, Fu, Maosheng, Zhou, Xiancun, Huang, Zhendong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 04.06.2025; MDPI
• Subjects: Accuracy; Algorithms; artificial rabbit optimization algorithm; Autonomous vehicles; BP neural network; Calibration; Computer vision; Global positioning systems; GPS; Indoor environments; line-of-sight; Localization; Location based services; Mathematical optimization; Neural networks; non-line-of-sight; Optimization; Performance evaluation; R&D; Radio frequency identification; Research & development; Signal processing; UWB; non-line-of-sight; UWB; BP neural network; artificial rabbit optimization algorithm; line-of-sight
• ISSN: 2313-7673; 2313-7673
• DOI: 10.3390/biomimetics10060367

In the field of ultra-wideband (UWB) indoor localization, traditional backpropagation neural networks (BPNNs) are limited by their susceptibility to local minima, which restricts their ability to achieve global optimization. To overcome this challenge, this paper proposes a novel hybrid algorithm, termed ARO-BP, which integrates the Artificial Rabbit Optimization (ARO) algorithm with a BPNN. The ARO algorithm optimizes the initial weights and thresholds of the BPNN, enabling the model to escape local optima and converge to a global solution. Experiments were conducted in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments using a four-base-station configuration. The results demonstrate that the ARO-BP algorithm significantly outperforms traditional BPNNs. In LOS conditions, the ARO-BP model achieves a localization error of 6.29 cm, representing a 49.48% reduction compared to the 12.45 cm error of the standard BPNN. In NLOS scenarios, the error is further reduced to 9.86 cm (a 46.96% improvement over the 18.59 cm error of the baseline model). Additionally, in dynamic motion scenarios, the trajectory predicted by ARO-BP closely aligns with the ground truth, demonstrating superior stability. These findings validate the robustness and precision of the proposed algorithm, highlighting its potential for real-world applications in complex indoor environments.