基于MCS-SCKF的超宽带室内定位算法

Ultra-wideband indoor positioning algorithm based on MCS-SCKF

针对传统超宽带(UWB)室内定位中非线性跟踪问题,基于当前统计(CS)模型和容积卡尔曼滤波(CKF),本文提出了一种新的定位算法。即采用奇异值分解(SVD)代替标准CKF算法中的Cholesky分解,提高了算法的稳定性,构造了奇异值分解容积卡尔曼滤波器(SCKF)。首先在CS模型的基础上改进了先验参数的函数形式,得到改进的CS模型(MCS),实现模型参数的自适应调整;然后将MCS模型引入SCKF滤波器,实现滤波算法的自适应调整;最后利用MCS-SCKF算法对UWB定位系统模型进行解算,从而得到移动目标位置。仿真和试验结果表明,该算法优于CS模型-卡尔曼滤波算法(CS-KF)和CS模型-SCKF算法(CS-SCKF),提高了UWB室内定位的定位精度。

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.