面向非视距环境的室内定位算法

节点位置信息在无线传感器网络中起着至关重要的作用.大多数定位算法在视距(Line-of-Sight,LOS)环境下能够取得较高的定位精度,然而在非视距(Non-Line-of-Sight,NLOS)环境下,由于障碍物的阻挡,无法取得理想的定位精度.针对室内环境中普遍存在的非视距传播现象,提出了基于RTT(Round Trip Time)和AOA(Angle Of Arrival)混合测距方式的室内定位方法,一种轻量级基于网格的聚类算法(Lightweight Grid-Based Cluster,LGBC)被用来生成移动节点的定位区域.算法不需要获取室内环境的先验信息.仿真结果表明,LGBC算法复杂度低,计算开销小,并且与同类算法相比,定位精度提高约65%.     

TOA-Based NLOS Error Mitigation Algorithm for 3D Indoor Localization

In the process of indoor localization,the existence of the non-line of sight(NLOS)error will greatly reduce the localization accuracy.To reduce the impact of this error,a 3 dimensional(3D)indoor localization algorithm named LMR(LLS-Minimum-Residual)is proposed in this paper.We first estimate the NLOS error and use it to correct the measurement distances,and then calculate the target location with linear least squares(LLS)solution.The final nodes location can be obtained accurately by NLOS error mitigation.Our algorithm can work efficiently in both indoor 2D and 3D environments.The simulation results show that the proposed algorithm has better performance than traditional algorithms and it can significantly improve the localization accuracy.     

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。     

基于CFR虚拟阵列天线的AOA室内定位

准确地估计信号的到达角（Angle Of Arrival,AOA）为实现在室内高精度定位提供了可能,为了能够准确地估计室内多径信号的AOA,并提取出直射路径的AOA信息进行定位,本文提出一种利用信道频率响应信息（Channel Frequency Response,CFR）扩展阵列天线的亚米级室内定位系统.首先,采集CFR信息进行AOA和信号到达时间（Time Of Arrival,TOA）的联合估计;其次,提出了一种基于AOA和TOA二维聚类信息的直射路径识别算法;另外,还提出了可视环境（Line Of Sight,LOS）以及非可视环境（Non Line Of Sight,NLOS）的识别算法,可以准确的判断出当前接收机相对发射机是处于LOS还是NLOS环境;最后,利用现有的三天线Wi-Fi设备在室内进行了测角以及定位测试,实验结果表明本文提出的定位系统在室内LOS和NLOS环境下分别可以达到中值误差为0.8m,1.3m的定位精度,可用于室内高精度定位.     

LTE中抑制非视距误差的分层协同定位算法

针对长期演进技术(LTE)定位系统中非视距(NLOS)误差会使得用户终端测得的定位参数存在较大偏差,从而导致基于观测到达时间差(OTDOA)定位技术所估计的用户终端位置定位精度下降,以及传统Chan算法和Taylor级数展开算法中OTDOA协方差矩阵难以获取的问题,本文提出了一种抑制NLOS误差的分层协同定位算法(HCLA, hierarchical collaborative localization algorithm)。算法首先鉴别出含NLOS误差的基站,然后利用残差加权算法获取OTDOA协方差矩阵,再对传统的Chan算法和Taylor级数展开算法进行改进,将二者联合起来对用户终端进行分层协同定位。该算法无需知道OTDOA误差先验信息。仿真结果表明,在NLOS环境下该算法能准确鉴别出含NLOS误差的基站,并能有效减小定位误差。     

Joint Particle Filter and UKF Position Tracking in Severe Non-Line-of-Sight Situations

The performance of localization techniques in a wireless communication system is severely impaired by biases induced in the range and angle measures because of the non-line-of-sight (NLOS) situation, caused by obstacles in the transmitted signal path. However, the knowledge of the line-of-sight (LOS) or NLOS situation for each measure can improve the final accuracy. This paper studies the localization of mobile terminals (MT) based on a Bayesian model for the LOS-NLOS evolution. This Bayesian model does not require having a minimum number of LOS measures at each acquisition. A tracking strategy based on a particle filter (PF) and an unscented Kalman filter (UKF) is used both to estimate the LOS-NLOS situation and the MT kinetic variables (position and speed). The approach shows a remarkable reduction in positioning error and a high degree of scalability in terms of performance versus complexity.     

基于粒子滤波和交互多模型的移动定位方法

为提高非视距情况下移动辐射源的定位精度,提出一种改进的交互粒子滤波算法.该算法对目标运动多模型和信号到达时间差测量噪声分布多模型联合建模.在交互多模型状态更新中利用粒子滤波对目标时变状态以及视距/非视距混合信道参数进行估计,抑制了非视距测量误差对移动定位的影响.仿真结果表明,改进算法的性能要优于现有的视距条件运动多模型和视距/非视距条件单一运动模型的定位算法,并且定位误差接近于推导的后验克拉美劳下界.     

非视距环境下基于二阶锥规划的RSS定位算法

针对无线传感器网络在非视距(NLOS)环境下利用接收信号强度(RSS)定位存在精度不足的问题,提出了一种新的基于二阶锥规划(SOCP)的鲁棒性定位算法。在假定非视距偏差上界的基础上构建了对非视距偏差量具有鲁棒性的定位方程,从而抑制了非视距偏差的干扰;接着利用凸优化技术将鲁棒性的定位问题转化为二阶锥规划问题,达到精确估计的目的,进而提高定位精度;此外,将定位问题推广到未知发射功率的情况,提出了一个迭代SOCP的算法。仿真结果表明,所提出的算法有效地解决了非视距定位中存在的问题,且定位精度要优于以往的牛顿迭代法、UT法以及SOCP法。     

Hybrid seagull and thermal exchange optimization algorithm‐based NLOS nodes detection technique for enhancing reliability under data dissemination in VANETs

The reliability of data dissemination in vehicular ad hoc network (VANET) necessitates maximized cooperation between the vehicular nodes and the least degree of congestion. However, non‐line of sight (NLOS) nodes prevent the establishment and sustenance of connectivity between the vehicular nodes. In this paper, a hybrid seagull and thermal exchange optimization (TEO) algorithm‐based NLOS node detection technique is proposed for enhancing cooperative data dissemination in VANETs. It inherits three different versions of the proposed hybridized algorithm; three different approaches for localization of NLOS nodes depending upon its distance from the reference nodes are incorporated. It is considered as a reliable attempt in effective NLOS node localization as it is predominant in maintaining the balancing the degree of exploration and exploitation in the search process. In the first variant, the method of the roulette wheel is utilized for selecting one among the two optimization algorithm. In the second adoption, this hybridization algorithm combines TEO algorithm only after the iteration of SEOA algorithm. In the final adoption, the predominance of the seagull attack mode is enhanced by including the heat exchange formula of TEO algorithms for improving exploitation capability. The simulation experiments of the proposed HS‐TEO‐NLOS‐ND scheme conducted using EstiNet 8.1 exhibited its reliability in improving the emergency message delivery rate by 14.86%, a neighborhood awareness rate by 13%, and the channel utilization rate by 11.24%, compared to the benchmarked techniques under the evaluation done with different number of vehicular nodes and NLOS nodes in the network.     

一种改进的无线定位算法

Taylor级数展开法和Chan算法是两种性能优良的利用电波到达时间差(TDOA)的定位算法,前者简单实用,但是其缺点是对初值比较敏感；后者在视距(LOS)传播环境下有较高的定位精度,二者结合可以大大提高定位的精度。但是在非视距(NLOS)传播环境下Chan算法精度会受到较大的影响,从而影响到Taylor级数展开法的定位精度。本文根据NLOS传播环境下附加传播时延和均方根时延扩展的统计特性,对NLOS误差的均值和方差进行估计,对TDOA测量值进行修正,采用Chan算法计算初值,再利用Taylor级数展开法进行定位,并与其它两种基于Taylor级数展开法的定位方法结果进行了比较。仿真结果表明,该算法能够提高NLOS传播环境下的定位精度,性能优于另外两种基于Taylor级数展开法的定位方法。     

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.     

严重遮挡非视距环境下的三维定位方法

在严重遮挡非视距环境中,由于定位源与未知节点之间被障碍物遮挡而无法检测直射路径,极大地制约了无线定位方法的应用。提出一种能够规避直射路径遮挡现象的三维定位框架,利用单次反射路径和方位信息,论证了定位源、未知节点和散射体的三维空间位置关系,提出了基于最小二乘准则的空间位置优化算法,并推导出空间位置的求解方法。同时对定位算法进行均方根误差（RMSE, root mean-square error）的理论分析。通过蒙特卡罗仿真实验分析了三维定位框架中距离和方位偏差对算法性能的影响,仿真结果与算法的RMSE理论结果相符,说明了三维非视距定位算法的有效性。     

NLOS error mitigation with information fusion algorithm for UWB ranging systems

This article puts forward a scalar weighting information fusion (IF) smoother with modified biased Kalman filter (BKF)and maximum likelihood estimation (MLE) to mitigate the ranging errors in ultra wid...     

基于非视距误差直接估计的定位跟踪算法

在蜂窝无线定位中,由于非视距（non-line-of-sight, NLOS）误差是影响定位精度的主要因素之一,所以如何减轻NLOS误差影响成为当前无线定位研究的热点。本文针对NLOS环境下的定位跟踪问题,提出一种基于扩展卡尔曼滤波（extended Kalman filter ,EKF)的定位跟踪算法。该算法首先在最小二乘准测下推导出估计测量值中NLOS误差的直接计算公式,然后使用约束加权最小二乘（constrained weighted least squares, CWLS）方法计算出每一个测量值中所含的NLOS误差,最后利用NLOS误差估计值去修正EKF滤波,以便适应NLOS环境下的定位跟踪,并获取高的定位精度。这种方式不依赖于特定的NLOS误差分布,也无需视距（line-of-sight, LOS）和非视距识别。数值结果表明该算法相比较于经典EKF算法和基于NLOS迭代的EKF算法可以快速有效地抑制定位误差,并且可以在极为恶劣的NLOS环境下满足FCC的定位要求。另外,复杂性实验表明该算法可适用于实时跟踪。      

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.     

LOS/NLOS混合环境中的粒子滤波跟踪算法

扩展卡尔曼滤波算法在直达波(Line-Of-Sight,LOS)和非直达波(None-Line-Of-Sight,NLOS)混合环境中存在显著的误差。该文根据混合噪声概率密度函数的数值近似公式,提出了一种基于传播环境LOS/NLOS二元状态信息的粒子滤波算法。仿真结果表明,利用了二元环境信息和混合噪声密度的粒子滤波算法能明显改善对移动目标的跟踪估计精度。     

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。     

基于B-LM圆环模型的NLOS信息约束单基站定位算法

针对当前室外蜂窝网多基站定位需要基站之间时间同步、数据同步的要求,以及NLOS环境造成的非服务区基站的信号可测性问题,该文提出基于B-LM圆环模型的NLOS信息约束单基站定位算法。首先根据散射体、目标和基站间的几何位置关系以及NLOS多路径信息构建定位方程,然后将定位方程转化为最小二乘优化问题,之后基于LM算法海森矩阵修正思想和拟牛顿2阶偏导构造思想提出B-LM算法,保证算法收敛于最优解,以得到目标位置。仿真结果表明,所提单基站定位算法能在宏蜂窝NLOS环境实现较高的定位精度。     

Single threshold optimization and a novel double threshold scheme for non‐line‐of‐sight identification

Non‐line‐of‐sight (NLOS) identification is a critical issue in wireless location system. The traditional method only considers the probability of false alarm to obtain the single threshold to identify the wireless propagation environment as line of sight (LOS) or NLOS. However, the probability of missed detection is rarely discussed. In this paper, firstly, the optimal single threshold is derived through minimizing the total error probability, which is the sum of the probability of false alarm and the probability of missed detection. Further, if the prior probabilities for the LOS and NLOS environment are available, a novel double threshold method based on the optimal single threshold is proposed to perform NLOS identification. By comparing the total error probability, we show through simulation results that the performance of the optimal single threshold is better than that of the traditional single threshold. Moreover, the novel double threshold NLOS identification scheme is feasible and better than the optimal single threshold scheme. It is also shown that the analytical results are consistent with the simulated ones.Copyright © 2012 John Wiley & Sons, Ltd.     

A decision tree‐based NLOS detection method for the UWB indoor location tracking accuracy improvement

Among existing wireless technologies, ultra‐wideband (UWB) is the most promising solution for indoor location tracking. UWB has a great multipath fading immunity; however, great multipath resolvability alone does not eliminate the effect of non‐line‐of‐sight (NLOS) and multipath propagation. NLOS and multipath propagation in indoor environments can easily produce meters of UWB ranging error. This condition gives an enormous impact on the accuracy of indoor location tracking data. To address this problem, we propose an NLOS detection method using recursive decision tree learning. Using the UWB channel quality indicators information, we develop our model with the Gini index and altered priors splitting criteria. We then validate the constructed model using the 10‐fold cross‐validation method. Our experiment shows that the constructed model has correctly detected 90% of both line‐of‐sight (LOS) and NLOS cases on the seven different indoor environments. The result of this work can be used for the UWB indoor location tracking accuracy improvement.