两种NLOS误差消除及TOA定位算法  

Two NLOS error elimination and TOA location algorithms

作　　者：段凯宇[1] 张力军[2] 高玲[1] 乔加新[1] 武凌[1] 

机构地区：[1]安徽财经大学信息工程学院,蚌埠233041 [2]南京邮电大学通信与信息工程学院,南京210003

出　　处：《信号处理》2008年第4期565-568,共4页Journal of Signal Processing

基　　金：安徽省高校自然科学研究项目(KJ2008B088)资助课题

摘　　要：在蜂窝网络定位中,由于NLOS环境造成的附加时延(NLOS误差)是导致定位精度下降的主要原因,本文将NLOS误差与系统测量误差合成的噪声分为均值部分和随机部分,利用卡尔曼滤波算法输出与噪声方差无关的特性,无需得到全部噪声方差的准确值,只利用系统测量噪声的方差,用卡尔曼滤波算法除随机部分,再根据噪声均值部分与移动台到基站距离的关系,提出了一种简单的最小二乘(LS)定位算法,或利用最优化方法进行定位;利用仿真实验得到滤波距离——误差先验信息,基于先验信息提出了第二种NLOS误差消除算法,再利用所提的最小二乘定位算法进行定位。仿真结果表明,本文提出的算法能够有效消除NLOS误差带来的影响,具有更高的定位精度与稳健性。In cellular network location,the excessive time delay caused by NLOS environment （NLOS error） is the main reason which degrades the location accuracy mainly. This paper separates the combined noise of the NLOS error and the system measurement error into mean value and stochastic part. The Kalman filtering algorithm has the property that its＇ output has no relationship with the variance of the noise. According to this property, the stochastic part can be eliminated by the Kalman filtering algorithm needing only the variance of the system measurement rather than the exact variance of the combined noise. Then the location of the mobile station （MS） can be estimated by using a proposed least square （LS） method or optimization algorithm according to the relationship of the mean value and the distance between the MS and the base station（BS）. This paper also summarizes the a priori information about filtered distanceerror in the light of the results of computer simulations. The second NLOS error elimination algorithm is proposed based on the a priori information. The computer simulations indicate the proposed algorithms can eliminate the NLOS error effectively with higher accuracy and robustness.

关 键 词：非视距 卡尔曼滤波器 到达时间 

分 类 号：O4-33[理学—物理] P228[天文地球—大地测量学与测量工程]