Ji Pengfei, Duan Zhongxing, Xu Weisheng
College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710311, China.
Sensors (Basel). 2024 May 16;24(10):3165. doi: 10.3390/s24103165.
Aiming at the problem that ultra-wide band (UWB) cannot be accurately localized in environments with large noise variations and unknown statistical properties, a combinatorial localization method based on improved cubature (CKF) is proposed. First, in order to overcome the problem of inaccurate local approximation or even the inability to converge due to the initial value not being set near the optimal solution in the process of solving the UWB position by the least-squares method, the Levenberg-Marquardt algorithm (L-M) is adopted to optimally solve the UWB position. Secondly, because UWB and IMU information are centrally fused, an adaptive factor is introduced to update the measurement noise covariance matrix in real time to update the observation noise, and the fading factor is added to suppress the filtering divergence to achieve an improvement for the traditional CKF algorithm. Finally, the performance of the proposed combined localization method is verified by field experiments in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, respectively. The results show that the proposed method can maintain high localization accuracy in both LOS and NLOS scenarios. Compared with the Extended Kalman filter (EKF), unbiased Kalman filter (UKF), and CKF algorithms, the localization accuracies of the proposed method in NLOS scenarios are improved by 25.2%, 18.3%, and 11.3%, respectively.
针对超宽带（UWB）在噪声变化大且统计特性未知的环境中无法精确定位的问题，提出了一种基于改进容积卡尔曼滤波（CKF）的组合定位方法。首先，为了克服在通过最小二乘法求解UWB位置的过程中，由于初始值未设置在最优解附近而导致局部逼近不准确甚至无法收敛的问题，采用列文伯格-马夸尔特算法（L-M）对UWB位置进行最优求解。其次，由于UWB和惯性测量单元（IMU）信息是集中融合的，引入一个自适应因子实时更新测量噪声协方差矩阵以更新观测噪声，并添加渐消因子抑制滤波发散，从而对传统CKF算法进行改进。最后，分别通过视距（LOS）和非视距（NLOS）场景下的现场实验验证了所提出的组合定位方法的性能。结果表明，该方法在LOS和NLOS场景下均能保持较高的定位精度。与扩展卡尔曼滤波器（EKF）、无偏卡尔曼滤波器（UKF）和CKF算法相比，该方法在NLOS场景下的定位精度分别提高了25.2%、18.3%和11.3%。
