基于WLSE-KF的传感器融合定位算法研究

多传感器融合在定位中的应用越来越广泛。在利用这些传感器进行定位的过程中,需要对其采集的数据进行融合。射频识别融合定位一般采用最小二乘法,然而,它可能使定位误差较大。文中提出了将最小二乘和卡尔曼滤波相结合的算法。该算法先利用加权最小二乘估计获得移动用户的初步位置,再利用扩展卡尔曼滤波进一步使定位精度得到提高。仿真结果表明该算法相比多种传统定位算法,误差减少,定位精度明显提高。

WLSE-KF-based Multi-Sensor Integration Location Algorithm

Abstract: Multi-sensor integration methods have been widely used in the positioning application .Particularly for the latter,Least Squares (LS) algorithms have been introduced in the integration. Generally, people use the LS to obtain the locations based on RFID. However, it may lead to noticeable positioning errors. In this paper, we propose an accurate algorithm by combing the weighted LS with Extended Kalman Filtering (EKF) algorithms for RFID integration location. First, we use LS to estimate the preliminary positions of the mobile user. And then the EKF algorithms are adopted tofurtherize the accuracy of these positions.Simulation results show that the proposed algorithm can reduce positioning errors, while improve the positioning accuracy.