LOS_NLOS下基于PF和最大似然的TDOA定位算法  

Location Algorithm Based on Particle Filtering and Maximum Likelihood for TDOA under LOS_NLOS Conditions

作　　者：黄波[1] 李伟 HUANG Bo;LI Wei(College of Telecommunications&Information Engineering,Nanjing Universityof Posts and Telecommunications Nanjing Jiangsu 210003,China)

机构地区：[1]南京邮电大学通信与信息工程学院,江苏南京210003

出　　处：《计算机仿真》2022年第3期481-484,489,共5页Computer Simulation

摘　　要：室内定位复杂环境中存在LOS和NLOS场景相互切换问题,对定位算法的连续性和精度提出了更高的要求。针对上述问题,提出一种基于粒子滤波和最大似然的TDOA定位算法:首先不区分LOS和NLOS场景的采用近似最大似然TDOA算法,获取一个初步的定位结果;然后对初步获取的结果用粒子滤波进行纠正,以减少LOS和NLOS场景切换下的引起的定位误差增大和定位结果不一致的问题。仿真结果表明,同样场景下,采用粒子滤波+TDOA定位算法,优于无迹卡尔曼滤波+TDOA算法的定位结果,在LOS和NLOS场景下都能获取较高的定位精度。In the complex indoor location environment,there is a problem of switching between LOS and NLOS conditions,which puts forward higher requirements for the continuity and accuracy of the location algorithm.To solve the above problem,This paper presents a TDOA location algorithm based on particle filter and maximum likelihood.Firstly,the approximate maximum likelihood TDOA algorithm was used to obtain a preliminary location result without distinguishing between LOS and NLOS scenes;Then,the preliminary results were corrected by particle filter to reduce the increase of positioning error and inconsistency of positioning results caused by LOS and NLOS scene switching.The simulation results show that the particle filter+TDOA positioning algorithm is better than the unscented Kalman filter+TDOA algorithm in the same scene,and can obtain high positioning accuracy in LOS and NLOS scenes.

关 键 词：室内定位 非视距 到达时间差 粒子滤波 无迹卡尔曼滤波 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]