一种基于散射体模型的NLOS误差抑制方法

在无线定位中,非视距（NLOS）传播成为高精度定位的主要障碍,它严重约束了无线移动台的定位精度。提出了一种新颖的减小非视距误差的方法。该基于到达时间（TOA）分布的定位算法是以多径散射为测量模型的。通过匹配由散射体模型产生的若干多径信号的TOA测量值的统计方法,可以获得视距（LOS）条件下基站与移动台之间的TOA估计值。由此,获得的LOS的TOA估计值可以用于任何传统的TOA定位算法。在NLOS环境中,该算法表明TOAA统的定位精度得到显著的提高。     

一种基于随机森林的LOS/NLOS基站识别方法

蜂窝移动通信环境复杂多变,在基站和移动台之间不可避免会出现电波的非视距（Non-Line-of-Sight,NLOS）传播,使基站和移动台之间的距离测量误差显著增大,导致定位性能急剧下降。为了准确识别出视距（Line-of-Sight,LOS）与非视距传播的基站信号,提出了一种基于随机森林的LOS/NLOS基站识别方法,通过分析移动台与各基站接收机测量距离与定位误差之间的相关性,选择LOS/NLOS测量距离作为特征进行分类器训练,再将分类器用于LOS/NLOS基站的识别。仿真结果表明,该方法对NLOS基站的正确识别率达到90%以上,能取得较好的定位性能。     

TOA数据处理对NLOS环境下定位精度的改善

基于移动通信环境中非视距(NLOS)传播时延服从指数分布的特性,提出了一种改善移动台定位精度的波达时间(TOA)数据处理方法.NLOS传播时延是TOA测量误差的一部分,是基站与移动台距离的指数函数,具有正偏置的特性,因此TOA测量值越大其误差越大.对所有的TOA测量数据进行分析,仅保留误差最小的3个,然后再采用最小二乘(LS)法估计移动台的坐标.仿真结果表明,该TOA数据处理方法能够明显改善NLOS传播环境下的定位精度,在系统测量误差较小时对LOS传播条件下的定位精度几乎没有影响.     

基于参数重构的非直达波混合定位算法

在蜂窝无线定位中,非视距(non-line-of-sight,NLOS)误差是影响定位精度的主要因素之一,故如何减轻NLOS误差影响是无线定位研究的热点。本文针对NLOS环境下的定位问题,提出基于参数重构的混合定位算法。首先利用波达角（angle-of-arrival,AOA）重构方法重构直达波AOA,随后充分利用AOA的重构结果,以最大似然估计法迭代估计直达波的波达时延（time-of-arrival,TOA）,最后利用这两个重构后的参数以视距（line-of-sight,LOS）混合定位方法估计出移动台的位置,以实现NLOS环境下的单基站定位,并获取较高的定位精度。这种方式无需视距与非视距识别,改进了传统的单目标参数重构模式。仿真结果验证了该算法的有效性。      

基于TOA的置信因子移动定位算法

移动台的精确定位面临的一个主要问题就是信号的非视距(NLOs)传输。小文在TOA测量距离的基础上,利用基站和测量值之间的几何关系、信号的损耗模型和信号强度信息,提出了置信因子定位算法(BFA)。研究了BFA算法在不同类型NLOS误差下的表现,结果表明该算法能够有效地降低NLOS误差的影响,定位精度比其它算法有显著提高,而且在不同分布的NLOS误差下表现稳定。     

适用于NLOS传播环境的几何定位方法

基于非视距传播(NLOS)的影响,移动通信环境中波达时间(TOA)的测量误差具有正偏置的特性,本文提出了一种简单的几何定位方法。该方法根据基站分布和TOA测量数据确定出若干个定位点,对这些点的坐标取平均值即得到移动台的位置估计。仿真结果表明,该方法能够有效提高NLOS传播环境下的定位精度,对LOS传播条件下的定位精度影响小。     

一种新的TOA无线定位算法

在无线通信系统中,精确定位面临的一个主要问题就是信号的非视距传播(NLOS)。提出了一种新的定位算法-最小距离误差算法(LRE),能够有效地降低NLOS误差的影响。算法基于到达时间(TOA)测量距离,利用TOA测量值所确定的几何关系,由受条件约束的优化方法求得移动台的估计位置。研究了该算法在不同类型NLOS误差下的表现,实验表明LRE算法的定位精度达到E911要求,比其它算法有明显提高,且在多种分布的NLOS误差下表现稳定。     

蜂窝无线网中的写作定位算法研究

在蜂窝无线网络中,单基站定位是最简单易行的定位方法,其需要利用基站测量移动终端信号的到达时间(TOA,time of arrival)和到达角(AOA,Angle of Arrival)信息.但是非视距(NLOS,Non Line of Sight)误差是影响定位精度的主要原因,为了抑制NLOS对定位精度的影响,提出了一种利用移动终端和中继节点之间的协作信息来提高定位精度的算法.该算法通过处理基站测量的TOA/AOA信息,以及移动终端到中继节点的TOA信息,利用中继节点与MS间的协作信息对估计的位置进行修正.仿真分析结果表明,在单基站蜂窝网中,提出的算法不仅整体提高了系统定位性能,而且在MS距离基站比较远的情况下,提出的协作定位算法的定位精度比现有的算法有明显提高.     

基于扩展卡尔曼滤波的非视距误差消除算法

在蜂窝网无线定位中,到达时间(TOA)或到达时间差(TDOA)中的非视距(NLOS)误差会导致移动台的位置估计出现较大偏差.为了减轻NLOS误差的影响,提出了一种基于扩展卡尔曼滤波(EKF)的非视距误差消除算法.算法通过引入一个NLOS转换因子改进EKF的迭代过程,消除NLOS误差对定位估计的影响.计算机仿真结果表明,在NLOS环境下定位精度的提高是显著的.     

一种有效减小非视距传播影响的TOA定位方法

本文基于移动通信环境中非视距(NLOS)传播时延服从指数分布的特性,提出了一种有效减小NLOS影响的定位方法.该方法首先利用测量的波达时间(TOA)和NLOS传播时延的统计特性估计由NLOS引起的附加时延;然后从测量的波达时间中减去附加时延,得到对LOS传播时间的估计,进而估计移动台的位置;最后,对不同时刻估计的移动台位置进行平滑处理,进一步减小NLOS的影响.采用该方法对移动台的位置进行的估计是一种无偏估计,不需要增加系统成本,计算简单,是一种非常实用的定位方法.仿真结果证明了该方法的有效性.     

Mobile Location Estimator in a Rough Wireless Environment Using Extended Kalman-Based IMM and Data Fusion

An extended Kalman-based interacting multiple model (EK-IMM) smoother is proposed for mobile location estimation with the data fusion of the time of arrival (TOA) and the received signal strength (RSS) measurements in a rough wireless environment. The extended Kalman filter is used for nonlinear estimation. The IMM is employed as a switch between the line-of-sight (LOS) and non-LOS (NLOS) states, which are considered to be a Markov process with two interactive modes. Combining extended Kalman filtering with the IMM scheme for accurately smooth range estimation between the corresponding base station (BS) and mobile station (MS) in the rough wireless environment, the proposed robust mobile location estimator, in association with data fusion, can efficiently mitigate the NLOS effects on the measurement range error. Simulation results illustrate that the performance of the proposed method has been significantly improved in the LOS/NLOS transition case. Moreover, the performance of the EK-IMM smoother with data fusion is also better than that with a single measurement used alone.      

Robust Mobile Location Estimator with NLOS Mitigation using Interacting Multiple Model Algorithm

A Kalman-based interacting multiple model (IMM) smoother is proposed for mobile location estimation with the time of arrival (TOA) measurement data in cellular networks to meet the Federal Communications Commission (FCC) requirement for phase 2. In this study, the line-of-sight (LOS) and non-line-of-sight (NLOS) conditions in cellular networks are considered as a Markov process with two interactive modes. Then we propose a Kalman-based IMM smoother to accurately estimate smooth range between the corresponding base station (BS) and mobile station (MS) in cellular networks. It is shown that the proposed mobile location estimator can efficiently mitigate the NLOS effects of the measurement range error even when the corresponding BS changes the condition between LOS and NLOS. Simulation results demonstrate that the performance of the proposed Kalman-based IMM smoother is improved significantly over the FCC target in both fixed LOS/NLOS and LOS/NLOS transition conditions     

Non-line of Sight Error Mitigation in Ultra-wideband Ranging Systems Using Biased Kalman Filtering

In this paper, a non-line of sight (NLOS) error mitigation method based on biased Kalman filtering for ultra-wideband (UWB) ranging is proposed. The NLOS effect on the measures of signal arrival time is considered one of the major error sources in range estimation and time-based wireless location systems. An improved biased Kalman filtering system, incorporated with sliding-window data smoothing and hypothesis test, is used for NLOS identification and error mitigation. Based on the results of hypothesis test, the estimated ranges are either calculated by smoothing the measured range when line of sight (LOS) status is detected, or obtained by conducting error mitigation on the NLOS corrupted measured range when NLOS status is detected. The effectiveness of the proposed scheme in mitigating errors during the LOS-to-NLOS and NLOS-to-LOS transitions is discussed. Improved NLOS identification and mitigation during the NLOS/LOS variations of channel status are attained by an adaptive variance-adjusting scheme in the biased filter. Simulation results show that the UWB channel status and the transition between NLOS and LOS can be identified promptly by the proposed scheme. The estimated time-based location metrics can be used for achieving higher accuracy in location estimation and target tracking.     

RSS协助的ray-tracing室内定位算法

针对室内定位,当信号受到非视距(non-line-of-sight, NLOS)和多径传播的影响时,本文提出一种接收信号强度(Received Signal Strength, RSS)协助的Ray-tracing室内定位算法,改进已经提出的基于虚拟基站方法的信号到达时间 (Time of Arrival, TOA)和信号到达角度(Direction of Arrival, DOA)室内无线信号Ray-tracing模型,利用信号RSS测量值优化算法,实现TOA、DOA和RSS协同定位,提高室内多径及非视距环境下,无线定位的精度,降低算法复杂度,提高算法处理信号多重散射的能力并降低了对基站的依赖性适用环境更为广泛。首先通过RSS得到信号源可能存在的位置,随后利用Ray-tracing原理并使用虚拟基站,将非视距路径定位问题转化为视距路径定位问题,利用TOA和DOA对直射、透射、反射和绕射情况进行分析建模,最后使用最小二乘法对可能的位置进行筛选,得到信号源的最终位置。仿真结果表明,本算法较改进前拥有更高的定位精度。      

A minimum entropy estimation based mobile positioning algorithm

The problem of locating a mobile terminal has received significant attention in the field of wireless communications. The wireless location problem is made difficult by nonsymmetric contamination of measured Time of Arrival (TOA) data caused by Non-Line-Of-Sight (NLOS) propagation. In this paper, a novel robust NLOS error mitigation algorithm based on minimum entropy estimation is proposed without prior statistics knowledge of NLOS propagation error.We compare the proposed algorithm with two additional ones, the Normal Least-Squares estimator and the Huber estimator, through MATLAB simulation in different COST 259 channel environment. Results reveal that the proposed algorithm is more robust to NLOS error than the other two, although it is not always superior to the other two on location accuracy.     

Hybrid TOA/AOA estimation error test and non‐line of sight identification in wireless location

In this paper, a modified time‐of‐arrival (TOA) estimation error test and a hybrid time‐of‐arrival/angle‐of‐arrival (TOA/AOA) estimation error test for identification of line of sight (LOS) base stations (BSs) are proposed. The proposed schemes aim to improve the location accuracy of wireless location systems suffering from the non‐line of sight (NLOS) propagation errors. The modified TOA‐based estimation error test is considered a straightforward approach in identifying the LOS‐BS set when the number of LOS BSs is greater than or equal to three. When both TOA and AOA metrics are available, hybrid TOA/AOA squares of normalized estimation errors are formulated by adopting the approximate maximum likelihood (AML) estimation. The proposed hybrid estimation error test scheme is capable of identifying the LOS/NLOS status of each BS, and performing location estimation in the situation where only two LOS BSs exist. Simulation results show that the proposed schemes are capable of correctly identifying the LOS BSs and improving the overall location accuracy. Copyright © 2009 John Wiley & Sons, Ltd.     

一种特殊GDOP场景下的NLOS传播识别算法

在地面无线定位中,影响定位精度的最大因素是电波的非视距( NLOS )传播误差,定位估计前识别收发信机之间电波是视距( LOS )还是NLOS传播是提升定位精度需要研究的重要课题。为此,先对一种基于交叉面积的NLOS 识别算法进行改进,然后提出了一种针对特殊几何精度因子( GDOP)场景下的NLOS识别算法———分步检验算法。该算法采用两步进行识别,先用数据检验筛选出测量样本中的LOS测量值,再用改进的交叉面积算法进行识别。仿真结果表明,分步检验算法在特殊GDOP场景下具有良好的识别性能。