多天线NLOS定位误差抑制算法

为抑制非视距传播造成的定位误差,提出一种基于对各基站TOA测量结果进行NLOS判别的误差抑制算法。与传统基于TOA统计信息的NLOS抑制不同,算法直接利用移动台多天线接收数据判别基站视距状态,然后融合LOS和NLOS基站测量结果解算移动台位置。NLOS判别机制采用多天线接收数据估计信道莱斯K因子,利用K因子在LOS/NLOS下服从的不同概率分布在信号处理层面对NLOS基站进行判别。算法最后采用约束最优化方法融合识别后的LOS和NLOS基站的TOA测量结果解算移动台位置。仿真结果表明,所提融合NLOS基站TOA解算算法可有效提高NLOS存在时的定位精度。      

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。     

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。      

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。     

用于修正NLOS误差的超宽带定位方法

近年来,基于超宽带系统的室内定位凭借其高精度和高稳定性等优点得到了广泛应用。在复杂室内环境中,超宽带信号在障碍物间的非视距传播导致定位基站和标签之间的距离测量值产生额外误差从而导致定位精度下降。文章提出一种用于修正非视距(NLOS)误差的超宽带定位方法,通过基于自适应增强算法识别 NLOS 传播,识别后通过测量值重构对应视距测量值并计算位置坐标,最后通过无迹卡尔曼滤波算法修正定位误差。实验结果表明,该算法有效消除了 UWB 定位系统中较大的 NLOS 误差,提高了定位精度,具有很好的稳定性。     

基于深度优先多径参数估计的NLOS定位增强算法

为了提高非视距（NLOS）环境中的毫米波系统定位精度，基于分布式压缩感知理论，提出一种深度优先的多路径参数估计算法。通过估计出来的多径参数来识别NLOS路径，增强了定位性能。首先，使用深度优先算法来减少非必要的路径搜索，获得更加准确的多径参数。其次，采用反向定位距离残差的方法进行NLOS多径识别。然后，对NLOS路径中的散射体进行匹配，估计出散射体的位置并将其视为虚拟锚节点。结合基站与虚拟锚节点的信息实现定位增强。最后，对所提算法的定位性能进行了仿真，与距离加权最小二乘（LS）算法和最大鉴别变换（MDT）算法相比，所提算法的性能分别提升了17%和8%。     

一种在NLOS环境下应用于NB-IoT系统的组合定位算法

传统非视距(NLOS)误差下无线定位的TDOA算法通常是减弱NLOS误差影响或直接提取LOS测量值,对于窄带物联网(NB-IoT)需要解决LOS与NLOS传输并存下的定位精度问题。文中采用最小二乘法对NLOS和LOS传播进行鉴别,然后对NLOS下的TDOA值进行优化处理。在此前提下,提出一种组合定位的算法,采用Taylor级数展开法计算LOS传输情况,用CHAN算法计算NLOS传输情况,再通过组合定位得到定位结果。仿真结果表明,改进后算法在NLOS的信道环境下的定位性能明显提高。     

Improved TOA‐Based Localization Method with BS Selection Scheme for Wireless Sensor Networks

The purpose of a localization system is to estimate the coordinates of the geographic location of a mobile device. The accuracy of wireless localization is influenced by non‐line‐of‐sight (NLOS) errors in wireless sensor networks. In this paper, we present an improved time of arrival (TOA)–based localization method for wireless sensor networks. TOA‐based localization estimates the geographic location of a mobile device using the distances between a mobile station (MS) and three or more base stations (BSs). However, each of the NLOS errors along a distance measured from an MS (device) to a BS (device) is different because of dissimilar obstacles in the direct signal path between the two devices. To accurately estimate the geographic location of a mobile device in TOA‐based localization, we propose an optimized localization method with a BS selection scheme that selects three measured distances that contain a relatively small number of NLOS errors, in this paper. Performance evaluations are presented, and the experimental results are validated through comparisons of various localization methods with the proposed method.     

两种非视线传播环境下的蜂窝系统定位算法

提供移动用户准确的定位业务是未来无线通信发展的必然趋势.但是由于非视线(NLOS)传播的存在,无线定位始终是一个难点.本文先介绍视线(LOS)传播时的定位方法,然后提出NLOS消除算法和虚拟基站移动NLOS消除方法.通过仿真和比较,说明两种方法获得较好的效果.     

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     

基于特征矢量的NLOS误差检测的定位算法

最小二乘估计算法常用于基于测距的源定位,然而,当移动基站与基站间呈非视距(Non Line of Sight, NLOS)路径时,最小二乘估计算法无法提供理想的定位精度。为了克服此问题,研究人员提出多类算法识别并消除NLOS误差。然而,现存的算法存在高运行时间的开销问题。为此,提出基于特征矢量的NLOS误差检测的定位 (Eigenvector-Based NLOS Error Identification Localization, E-NIL) 算法。E-NIL算法先利用基于测距数据的统计特性识别NLOS误差,然后,将NLOS误差看成确定加性噪声项,再利用误差函数与它的特征矢量间的互相关,寻找NLOS误差值。最后,再删除这些NLOS项,并依据这些无NLOS误差的数据估计移动基站的位置。实验数据表明,提出的E-NIL算法在定位精度和复杂度方面优于同类算法。     

Time-of-arrival based localization under NLOS conditions

Three or more base stations (BS) making time-of-arrival measurements of a signal from a mobile station (MS) can locate the MS. However, when some of the measurements are from non-line-of-sight (NLOS) paths, the location errors can be very large. This paper proposes a residual test (RT) that can simultaneously determine the number of line-of-sight (LOS) BS and identify them. Then, localization can proceed with only those LOS BS. The RT works on the principle that when all measurements are LOS, the normalized residuals have a central Chi-Square distribution, versus a noncentral distribution when there is NLOS. The residuals are the squared differences between the estimates and the true position. Normalization by their variances gives a unity variance to the resultant random variables. In simulation studies, for the chosen geometry and NLOS and measurement noise errors, the RT can determine the correct number of LOS-BS over 90% of the time. For four or more BS, where there are at least three LOS-BS, the estimator has variances that are near the Cramer--Rao lower bound.     

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.      

一种基于单次散射体定位的TOA/AOA混合定位算法

为了降低非视距(Non-Line-of-Sight,NLOS)误差对定位精度的不利影响,提出了一种基于散射体定位的到达时间(Time of Arrival,TOA)/到达角(Angle of Arrival,AOA)混合定位算法.假设各基站(Base Station,BS)接收的多径信号都经历了散射体单次散射,首先利用...