Efficient Localization Algorithm With UWB Ranging Error Correction Model Based on Genetic Algorithm-Ant Colony Optimization-Backpropagation Neural Network

• Published in: IEEE sensors journal Vol. 23; no. 23; pp. 29906 - 29918
• Main Authors: Dai, Peipei, Wang, Sen, Xu, Tianhe, Li, Min, Gao, Fan, Xing, Jianping, Yao, Linghan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adaptation models; Ant colony optimization; Ant colony optimization (ACO); Artificial neural networks; Back propagation; Back propagation networks; backpropagation neural network (BPNN); Distance measurement; Error correction; Error correction & detection; genetic algorithm (GA); Genetic algorithms; Inertial platforms; Kinematics; Location based services; Mathematical models; Neural networks; ranging error correction; Reliability; Sensors; Ultrawideband; ultrawideband (UWB); Wireless sensor networks
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2023.3327460

The development of wireless sensor network technology has extended the diverse range of tools available in location-based services (LBS). Indoor high-precision positioning is among the most popular topics in location tracking and positioning. Ultrawideband (UWB) double-sided two-way ranging (DS-TWR) is used widely because it provides a reliable ranging performance. In this study, the effect of the UWB DS-TWR ranging error was suppressed using a ranging error model to improve the reliability and accuracy of indoor positioning services. Based on the conditions described above, an optimized backpropagation neural network (BPNN) correction model that integrates both a genetic algorithm (GA) and the ant colony optimization (ACO) algorithm, forming the GA-ACO-BPNN model, is established and verified experimentally. In addition, under static and kinematic actual positioning conditions, improvements in the BPNN, GA-BPNN, ACO-BPNN, and GA-ACO-BPNN ranging error correction models in terms of their positioning performances are calculated and compared. The experimental results show that the proposed GA-ACO-BPNN model can reduce the impact of the ranging error on ranging and positioning effectively. The positioning accuracy and reliability of the UWB DS-TWR solutions are improved significantly after application of this model, which provides a reference point for solutions to subsequent fusion positioning problems, e.g., UWB and inertial measurement unit integration.