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

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, t...

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Vydáno v:Biomimetics (Basel, Switzerland) Ročník 10; číslo 6; s. 367
Hlavní autoři: Jia, Chaochuan, Tao, Can, Yang, Ting, Fu, Maosheng, Zhou, Xiancun, Huang, Zhendong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 04.06.2025
MDPI
Témata:
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
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Shrnutí: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.
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ISSN:2313-7673
2313-7673
DOI:10.3390/biomimetics10060367