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。      

一种NLOS环境下的TOA/AOA定位算法

为了减小NLOS传播的影响,基于几何结构的单次反射统计信道模型,该文提出一种NLOS环境下的TOA/AOA定位算法。利用RBF神经网络较快的学习特性和逼近任意非线性映射的能力,对NLOS传播的误差进行修正以减小NLOS传播的影响,再利用最小二乘(LS)算法进行定位,从而提高系统的定位精度。仿真结果表明,该算法在NLOS环境下有较高的定位精度,性能优于Chan算法,Taylor算法和LS算法。     

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.     

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.     

Non-line-of-sight error mitigating algorithm based on scattering models via multi-antenna system

In urban environment with serious blocking of direct paths, the non-line-of-sight （NLOS） propagation influences the location estimation accuracy. In this article, a novel algorithm is developed, which can mitigate the NLOS errors in location estimation significantly. Utilizing multiantenna array, the information of scatterers that cause the NLOS propagation is obtained. Then, we combine the information with TOA/TDOA based location algorithm to estimate the location of mobile station （MS）. The simulation results show that our method can mitigate NLOS errors and enhance the location accuracy greatly.     

基于TDOA无线定位的NLOS误差抑制算法

在无线定位算法中,非视距误差是影响定位精度的一个重要因素。为了减小NLOS误差的影响,文中提出了一种基于TDOA的NLOS误差抑制算法。该算法引入信息阈值和修正参数,对卡尔曼滤波迭代过程进行改进,有效地抑制了TDOA测量值中的NLOS误差。仿真结果表明,该算法在NLOS误差严重且误差模型不确定的环境下仍可保持较高的定位精度,且性能优于已有基于卡尔曼滤波的NLOS误差抑制算法。     

Ranking of TOA Measurements Based on the Estimate of the NLOS Propagation Contribution in a Wireless Location System

The presence of non-line-of-sight (NLOS) propagation is a key issue that limits the accuracy of wireless location systems. The lack of direct sight causes the measurements obtained by location systems to be so unpredictable that they can produce high inaccuracies in the estimation of the mobile station location. In this paper we propose a novel technique to improve location reliability and accuracy in cases where NLOS propagation is present. For that, in registers of time of arrival (TOA) measurements taken from each base station (BS) in view, we detect the presence of NLOS propagation and estimate the ratio of the measurements coming from NLOS propagation. With this estimate we can assess how much is NLOS propagation affecting the measurements taken from each BS and then we can identify the best measurements and BSs to achieve the highest accuracies in location.     

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。     

蜂窝移动定位系统定位精度提高算法的研究进展

提高蜂窝移动定位系统的定位精度是定位技术在CDMA系统中广泛应用的关键。针对提高移动定位精度算法的研究,分别从基本定位算法、非视距传播误差消除算法、抗多径干扰时延估计和抗多址干扰算法、数据融合定位方法等几个方面进行了详细的分析和讨论,综述了该领域的最新研究进展,提出了自己的观点。最后对移动定位精度提高算法研究的未来进行了展望。     

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。     

一种改进的NLOS环境下的TDOA/AOA混合定位算法

在蜂窝移动通信系统中,利用基站测量的到达时间差(TDOA)和电波到达角(AOA)的混合定位方法能够比传统的TDOA方法提供更高的定位精度。但是在非视距(NLOS)条件下,当AOA的测量误差超过一定值时,定位的误差仍然很大。该文根据NLOS传播环境下附加传播时延服从指数分布的特性,估计附加时延的均值和方差,对TDOA测量值进行重构,再以AOA方法进行辅助定位。仿真结果表明,该算法能显著提高传统的TDOA和TDOA/AOA方法在NLOS传播环境下的定位精度。     

Hybrid TOA–AOA Location Positioning Techniques in GSM Networks

Positioning algorithms and their implementation in mobile networks are being investigated in the literature due to their importance in location services. Nowadays, the need for superior accuracy has cast attention to hybrid positioning techniques. In this paper, we introduce a novel algorithm for the identification of NLOS propagation using both angle and time estimates, which leads to enhanced versions of the Time of Arrivals and Angle of Arrivals positioning methods. Furthermore, a novel GSM procedure for the implementation of the latter techniques is proposed. In contrast to specified network-based GSM solutions (U-TDOA), the proposed requires minimum modifications in the GSM Phase 2+ infrastructure and protocol stack, and therefore increases the upgrade flexibility and minimizes the implementation cost. The proposed GSM positioning procedure has been experimentally validated using a GSM emulator and the modified signalling messages given by a measurement tool of the emulator are exhibited. Finally, the enhanced cost functions are experimentally evaluated using several GSM-like, high-capacity simulation environments and the results have shown significant reduction of the location error compared to the conventional techniques.     

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。      

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%.     

NLOS identification for UWB localization based on import vector machine

Ultra-Wide Band (UWB) based localization is one of the most promising techniques for high accuracy localization. The crucial factor that aggravates the localization precision is None-Line-of-Sight (NLOS) propagation. To address this issue, we propose a novel NLOS identification algorithm with feature selection strategy and a localization algorithm based on Import Vector Machine (IVM) with high accuracy and low complexity. The feature selection strategy further meliorates the classification accuracy. The probability outputs of IVM is employed by the localization algorithm and yields higher positioning accuracy than its counterpart methods – Support Vector Machine (SVM) and Relevance Vector Machine (RVM). Simulation results prove that IVM is a robust and efficient method for NLOS identification and localization.     

TOA-based robust location algorithms for wireless cellular networks

Caused by Non-Line-Of-Sight (NLOS) propagation effect, the non-symmetric contamination of measured Time Of Arrival (TOA) data leads to high inaccuracies of the conventional TOA based mobile location techniques. Robust position estimation method based on bootstrapping M-estimation and Huber estimator are proposed to mitigate the effects of NLOS propagation on the location error. Simulation results show the improvement over traditional Least-Square (LS) algorithm on location accuracy under different channel environments.     

一种基于AP ID过滤的WLAN位置指纹定位算法

非视距传播是影响定位精度的主要因素,深入研究信号强度的统计特性有利于定位精度的提高。基于RSSI的位置指纹定位技术因其设备简单,定位精度高而成为近年来定位技术研究的焦点。在对信号强度的统计特性深入分析的基础上,结合AP ID与移动台位置之间的关系提出一种改进的基于AP ID过滤的无线局域网位置指纹定位算法。并借助MATLAB仿真对比了改进前后的算法性能,证明了改进算法的不仅降低了计算复杂度,且提高了定位精度。     

一种CDMA蜂窝定位中减小NLOS误差的算法

随着下一代移动通信研究的热潮,移动定位技术的研究也方兴未艾。在移动定位方程的解算中,测量噪声和NLOS误差是主要误差源,尤其是NLOS已经成为影响定位精度的主要因素。本文研究了CDMA系统中的NLOS误差减小问题,基于残余函数概念提出了ARWA算法,在NLOS路径不能有效识别的条件下,有效地减小了NLOS误差;仿真结果显示这种算法的定位精度明显强于一般LS估计。     

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.     

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。