针对室内定位精度低、易受多路径效应干扰、环境噪声大的问题，提出一种基于拓展卡尔曼滤波（EKF）的超宽带（UWB）+蓝牙低功耗技术（BLE）组合定位数据融合算法，首先推导了UWB双边双向测距（DS-TWR）模型、BLE对数距离路径衰减的测距模型和EKF融合UWB+BLE的数学模型，同时通过实验验证了新方法的可靠性和稳定性，实验数据表明：UWB+BLE的数据融合室内定位的精度优于单一UWB或BLE定位精度。使用UWB设备进行室内定位，定位最大误差、最小误差分别为30.76、6.44 cm，使用BLE设备进行室内定位，定位最大误差、最小误差分别为68.50、15.23 cm；与传统空间四点定位算法相比，EKF融合算法的定位结果在最大误差、最小误差、RMSE进度分别提升了46.63%、41.90%、42.00%。

To address the issues of low indoor positioning accuracy, susceptibility to multi-path interference, and high environmental noise, we proposed a positioning data fusion algorithm based on extended Kalman filter(EKF) for ultra wide band(UWB) and bluetooth low energy(BLE). Firstly, we derived the UWB double side-two way ranging(DS-TWR) model, the BLE logarithmic distance path attenuation ranging model, and the EKF fusion UWB+BLE mathematical model. Then, we verified the reliability and stability of new method through experiments. The experimental data show that the accuracy of UWB+BLE data fusion indoor positioning is superior to that of a single UWB or BLE positioning.Using UWB equipment for indoor positioning, the maximum and minimum positioning errors are 30.76 cm and 6.44 cm, respectively. Using BLE equipment for indoor positioning, the maximum and minimum positioning errors are 68.50 cm and 15.23 cm, respectively. Compared with traditional spatial four point positioning algorithms, the EKF fusion algorithm has improved the positioning results by 46.63%, 41.90%, and42.00% in terms of maximum error, minimum error, and RMSE progress, respectively.