基于最优传播路径的EMA/EKF定位算法研究

Research on EMA/EKF Localization Algorithm Based on Optimal Propagation Path

关键词:  室内定位  蓝牙  基站分布  EMA算法  EKF算法

Key words:  Indoor positioning  Bluetooth  Base station distribution  EMA algorithm  EKF algorithm

摘要:

针对室内环境下蓝牙系统易受非视距影响，采用蓝牙到达角（AOA）解决室内行人定位存在较大偏差的问题，研究基站几何分布和定位算法对定位精度的影响。关于蓝牙信号在传播过程中易衰减及产生多径等因素影响定位性能，对基站安装位置与信号接收范围关系进行研究，建立基站分布优化模型，使用遗传算法（GA）求解基站布设最优位置。在此基础上，提出了基于改进指数移动平均（EMA）算法结合扩展卡尔曼滤波（EKF）算法进行误差补偿。实验结果表明，该方法的平均定位误差为0.89m，相较于EKF定位精度更高，证明了所提方法的有效性。

Abstract:

In this paper, bluetooth angle of arrival (AOA) algorithm is taken into account to solve indoor based positioning problem that the bluetooth signals are prone to be affected by non-line-of-sight, which leads to large deviation of pedestrian positioning results， and the influences of the geometric distribution of base stations and positioning algorithms on positioning accuracy are studied. In the case of non-line-of-sight, the signal attenuation and multipath during bluetooth signal propagation affect the positioning accuracy. According to the relationship between the installation position of the base station and the signal receiving range, the base station distribution optimization model is established and the optimal location of base station layout is obtained by genetic algorithm (GA). On this basis, error compensation based on improved exponential moving average (EMA) algorithm combined with extended Kalman filter (EKF) algorithm is proposed. Experimental results show that, the average positioning error of the proposed method is 0.89m, which is more accurate than that of EKF, proving the effectiveness of the proposed method.