基于UWB/PDR的航向发散自适应修正算法研究

为解决航位推算（PDR）算法累积误差过大并且长航时航向发散的问题，提出了一种基于UWB/PDR自适应扩展卡尔曼滤波（EKF）融合算法。该算法通过UWB定位值和PDR实时解算位置得到自适应校准因子，通过在常规的EKF算法的基础上增加自适应校准因子动态调整UWB观测值的权重来校准位置误差。并用UWB的实时测距对PDR的航向发散进行周期性修正。实验结果表明：自适应EKF融合算法相较于纯PDR航向发散误差降低了63.9%，相较于标准EKF融合算法发散误差降低了31.1%，同时定位百米误差降至0.33m。

Research on adaptive correction algorithm of heading divergence based on UWB/PDR

To solve the problem of excessive cumulative error and long heading divergence in the position projection (PDR) algorithm, an adaptive extended Kalman filter (EKF) fusion algorithm based on UWB/PDR is proposed. The adaptive calibration factor can be achieved from the UWB positioning value and the PDR real-time position solution, and the position error is calibrated by dynamically adjusting the weight of the UWB observation with the adaptive calibration factor based on the conventional EKF algorithm. And the real-time ranging of the UWB is used to periodically correct the heading divergence of the PDR. The experimental results show the heading dispersion error is reduced by 63.9% with the adaptive EKF fusion algorithm compared to the pure PDR and 31.1% compared to the general EKF fusion algorithm, Moreover, the positioning 100m error is reduced to 0.33m.