基于无迹卡尔曼滤波的iBeacon/INS数据融合定位算法

针对微机电惯性导航系统(MEMS-INS)定位解算存在积累误差及低功耗蓝牙技术iBeacon指纹定位存在跳变误差等问题，该文提出一种基于无迹卡尔曼滤波器(UKF)的iBeacon/MEMS-INS数据融合定位算法。该算法对iBeacon锚点与定位目标的距离进行解算，利用加速度计和陀螺仪的数据实现姿态阵和位置解算。将蓝牙锚点位置向量、载体速度误差信息等组成状态量，将惯性导航定位信息和蓝牙定位距离信息等组成观测量，设计无迹卡尔曼滤波器，实现iBeacon/MEMS-INS数据融合定位。实验测试结果表明，该算法有效解决MEMS-INS存在较大积累误差及iBeacon指纹定位存在跳变误差的问题，可以实现1.5 m内的定位精度。

IBeacon/INS Data Fusion Location Algorithm Based on Unscented Kalman Filter

In order to overcome the accumulation error in Micro-Electro-Mechanical System-Inertial Navigation System (MEMS-INS) and the jump error in iBeacon fingerprint positioning, an iBencon/MEMS-INS data fusion location algorithm based on Unscented Kalman Filter (UKF) is proposed. The new algorithm solves the distance between the iBeacon anchor and the locating target. The solution of attitude matrix and position are obtained respectively by using accelerometer and gyroscope data. Bluetooth anchor position vector, the carrier speed error and other information constitute state variables. Inertial navigation location and bluetooth system distance information constitute measure variables. Based on state variables and measure variables, the UKF is designed to realize iBencon/MEMS-INS data fusion indoor positioning. The experimental results show that the proposed algorithm can effectively solve the problem of the large accumulation error of INS and the jump error of iBeacon fingerprint positioning, and this algorithm can realize 1.5 m positioning accuracy.