基于位置指纹与PDR融合的室内定位算法研究

Research on indoor positioning algorithm based on location fingerprint and PDR

运用位置指纹与行人航位推算(pedestrian dead reckoning,PDR)融合的方法研究室内定位算法,以提高室内定位精度。对于位置指纹算法,通过优化指纹数据库完成离线数据训练,通过限定区域加权K实现最优邻近法的在线实时匹配。对于行人航位推算法,提出自适应加权波峰检测算法检测步频,改进了步长估算的非线性模型,融合陀螺仪和磁力计信息进行航向估计。最终运用无迹卡尔曼滤波器对位置指纹和PDR进行融合,提高了定位精度,并在定位系统中进行了验证和应用。

To improve the indoor positioning accuracy,the methods of position fingerprint and pedestrian dead reckoning(PDR)are used to study the indoor positioning algorithm.For position fingerprint algorithm,the fingerprint database is optimized through the offline data training phase,and the optimization of the nearest neighbor algorithm is carried out by limiting the region weighted value K through the online real-time matching phase.For PDR algorithm,the self-adaptation peak detection algorithm used for step frequency detection is proposed.The improved nonlinear model is used for step size estimation.Moreover,The gyroscope’s information are fused to magnetometer’s information in the heading estimation.Finally,the unscented Kalman filter is used to fuse the position fingerprint algorithm and PDR method,which improves the positioning accuracy and the practicability of the fusion algorithm is verified by the positioning system.