A High-Accuracy 3-D Ultrawideband Positioning Approach Using Clustering Optimization Algorithm and Error-Correction Model

The high-accuracy positioning technology has attracted significant attention in 3-D indoor environment. Because of the unknown disturbance and random error, the positioning precision of ultrawideband (UWB) is severely compromised by the complex indoor environment. In this research, a high-accuracy 3-D UWB positioning approach is developed based on the clustering optimization algorithm and error-correction model. Using the particle swarm optimization (PSO) K-means clustering algorithm, the positioning space is divided into different clustering zones. Then, the parameters (predicted error P, process noise Q, and measurement noise R) of Kalman Filter (KF) function are optimized by the PSO algorithm with linear decreasing inertia weight strategy for different clustering zones. In addition, the error-correction model is established to reduce the random error. According to the results of indoor positioning experiments, the maximum mean error (ME) in the axis of the developed approach for experiment 1/2 is 7.89/8.12 cm, while that of the traditional positioning method is 11.76/8.31 cm. The root mean square error of the developed approach in experiment 1/2 is 15.55/14.69 cm, where those of the traditional approach is 21.28/16.08 cm. The mean absolute error (MAE) of the developed approach in experiment 1/2 is 6.86/6.02 cm, where those of the traditional positioning approach is 9.96/6.26 cm. Therefore, the developed approach can be used to improve the 3-D UWB positioning accuracy, and it is valuable in the smart factory, underground space, and logistics storage in indoor environment.