基于改进粒子滤波的超宽带与惯性测量单元融合定位算法

为了提高UWB与IMU融合定位算法的定位精度,提出了一种基于改进自适应加权数据融合的粒子滤波定位算法。首先利用自适应最优加权融合算法中最小方差估计理论,对粒子滤波中粒子分布权重进行调整；利用阈值限制所求观测方差,避免了因实际环境导致观测方差发散；利用观测噪声协方差和测量值,在粒子滤波后RMSE限制区间求得各传感器最优加权因子,避免因传感器信号弱或丢失产生的算法发散问题。最后,进行了UWB与IMU融合定位的对比实验,实验结果显示该算法与EKF融合算法相比定位精度提高了15%以上。

UWB and IMU Fusion Positioning Algorithm Based on Improved Particle Filtering

In order to improve the positioning accuracy of the fusion positioning algorithm of UWB and IMU, a particle filter positioning algorithm based on improved adaptive weighted data fusion was proposed. First, the minimum variance estimation theory in the adaptive optimal weighted fusion algorithm was used to adjust the particle distribution weight in particle filtering. The threshold was used to limit the observed variance to avoid divergence of the observed variance due to the actual environment. Using the observation noise covariance and measured values, the optimal weighting factors for each sensor were obtained in the RMSE limit interval after particle filtering to avoid the problem of algorithm divergence caused by weak or missing sensor signals. Finally, A comparative experiment of UWB and IMU fusion positioning was carried out. The experimental results show that the algorithm improves the positioning accuracy by more than 15% compared with the EKF fusion algorithm.