关键词: UWB, 室内定位, 容积卡尔曼滤波, 当前统计模型, 奇异值分解

Abstract: Aiming at the nonlinear tracking problem in traditional ultra-wideband (UWB) indoor positioning, this paper proposes a new positioning algorithm based on the current statistical (CS) model and volumetric Kalman filter (CKF). The localization algorithm uses singular value decomposition (SVD) to replace the Cholesky decomposition in the standard CKF algorithm to improve the robustness of the algorithm, constructing a singular value decomposition volumetric Kalman filter (SCKF). The functional form of the test parameters is obtained firstly, and the improved CS model (MCS) is obtained to realize the adaptive adjustment of the model parameters; then the MCS model is introduced into the SCKF filter to realize the adaptive adjustment of the filtering algorithm; finally, the MCS-SCKF algorithm can be used to The UWB positioning system model which is solved to obtain the moving target position. Simulation and experimental results show that the algorithm is superior to CS model-Kalman filter algorithm (CS-KF) and CS model-SCKF algorithm (CS-SCKF), and improves the positioning accuracy of UWB indoor positioning.

Key words: ultra-wideband, indoor positioning, volumetric Kalman filter, current statistical model, singular value decomposition