Bayesian method for NLOS mitigation in single moving sensor Geo-location

The problem of locating mobile sensors has received considerable attention, particularly in the field of wireless communications. It is well-known that the presence of non-line-of-sight (NLOS) errors in the geo-location problem leads to severe degradation in the localization performance. In this paper, we propose a robust Bayesian method to mitigate the NLOS errors in location estimation of a single moving sensor, whereby the localization is performed using time-of-arrival (TOA) measurements. This method is based on the Markov chain Monte Carlo (MCMC) approach. Numerical simulations results illustrate the promising results of our method in a mixed line-of-sight (LOS) and NLOS environment.     

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。     

Error performance and throughput evaluation of a multi-Gbps millimeter-wave WPAN system in the presence of adjacent and co-channel interference

This paper investigates the impact of adjacent channel interference (ACI) and co-channel interference (CCI) on error performance and throughput of a multi-Gbps millimeterwave wireless personal area network (WPAN) system in a realistic residential line-of-sight (LOS) and non-line-of-sight (NLOS) multipath environment. The main contribution of this paper is providing a multi-Gbps WPAN system design in the challenging multipath environment in the presence of ACI/CCI. Based on the investigation results, we have provided ACI/CCI rejection as a reference for victim receiver protection design. In the NLOS environment, the ACI rejection (i.e. ACI that causes 0.5 dB degradation in the required signal-to-noise ratio (SNR) to achieve bit error rate (BER) of 10^-6) for pi/2-BPSK, QPSK, 8 PSK and 16 QAM are 13, 7, 0 and -6dB respectively. And the CCI rejection for similar modulation schemes are -18, -20, -26 and -29 respectively. Secondly, we have clarified the LOS-NLOS relationship of the ACI/CCI impact to system performance. ACI in multipath NLOS environment causes an additional 5 dB degradation to error performance as compared to ACI in the LOS environment. CCI on the other hand, has similar impact on error performance in both LOS and NLOS environment. Thirdly, we have clarified the relationship between modulation spectral efficiency and robustness against ACI/CCI. In an environment with no or low ACI/CCI, the maximum achievable throughput for pi/2-BPSK, QPSK, 8 PSK and 16 QAM in LOS environment are 1.2, 2.5, 3.8 and 5 Gbps respectively. In NLOS environment, the achievable throughput decreases to 1, 1.9, 2.8 and 3.8 Gbps respectively. As ACI/CCI increases, the throughput of higherorder modulation schemes such as 16 QAM decreases the most rapidly, followed by 8 PSK and QPSK. The throughput for pi/2-BPSK has the highest tolerance against increasing ACI/CCI, at the expense of lower maximum achievable throughput.     

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。     

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.     

Improved Localization Algorithms Based on Reference Selection of Linear Least Squares in LOS and NLOS Environments

Linear Least Squares (LLS) estimation is a low complexity but sub-optimum method for estimating the location of a mobile terminal (MT) from some measured distances. It requires selecting one of the known fixed terminals (FTs) as a reference FT for obtaining a linear set of expressions. In this paper, the choosing of the reference FT is investigated. By analyzing the objective function of LLS algorithm, a new method for selecting the reference FT is proposed, which selects the reference FT based on the minimum residual (denoted as MR-RS) rather than the smallest measured distance and improves the localization accuracy significantly in Line of sight (LOS) environment. In Non-line of sight (NLOS) environment, we combine MR-RS algorithm with two other existing algorithms (residual weighting algorithm and three-stage algorithm) to form new algorithms, which also improve the localization accuracy comparing with the two algorithms. Moreover, the time complexity of the proposed algorithms is analyzed. Simulation results show that the proposed methods are always better than the existing methods for arbitrary geometry position of the MT and the LOS/NLOS conditions.     

Mobile Tracking in Mixed Line-of-Sight/Non-Line-of-Sight Conditions: Algorithm and Theoretical Lower Bound

The paper investigates the problem of mobile tracking in mixed line-of-sight (LOS)/non-line-of-sight (NLOS) conditions. The motion of mobile station is modeled by a dynamic white noise acceleration model, while the measurements are time of arrival (TOA). A first-order Markov model is employed to describe the dynamic transition of LOS/NLOS conditions. An improved Rao-Blackwellized particle filter (RBPF) is proposed, in which the LOS/NLOS sight conditions are estimated by particle filtering using the optimal trial distribution, and the mobile state is computed by applying approximated analytical methods. The theoretical error lower bound is further studied in the described problem. A new method is presented to compute the posterior Cramer-Rao lower bound (CRLB): the mobile state is first estimated by decentralized extended Kalman filter (EKF) method, then sigma point set and unscented transformation are applied to calculate Fisher information matrix (FIM). Simulation results show that the improved RBPF is more accurate than current methods, and its performance approaches to the theoretical bound.     

超宽带室内定位算法综述

在超宽带定位系统中,多径效应、NLOS 等因素会导致一定程度的测量误差。详细介绍了基于超宽带的各类定位技术及其实现原理,并着重于定位性能较好的TDOA技术,从定位模型建立到定位解算方面系统地介绍了各类基于TDOA的定位算法,详细阐述了各种算法的实现原理及设计流程,并对各种算法在LOS和NLOS下的性能进行了比较分析。     

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.      

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.     

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。      

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。     

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     

卷积码在UWB Rake接收机中的应用

针对有码间干扰的TH-PPM-UWB通信系统,研究了基于卷积码Rake接收机的性能,并在IEEE 15.3a UWB的LOS信道和NLOS信道下对采用卷积码Rake接收机的误比特率进行计算机仿真,仿真结果表明,当SNR达到一定值后,无论是LOS信道还是NLOS信道,相对未编码的Rake接收机,采用卷积码的Rake接收机性能明显提高,在NLOS信道更为显著。     

A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques

Localization of a wireless device using the time-ofarrivals (TOAs) from different base stations has been studied extensively in the literature. Numerous localization algorithms with different accuracies, computational complexities, a-priori knowledge requirements, and different levels of robustness against non-line-of-sight (NLOS) bias effects also have been reported. However, to our best knowledge, a detailed unified survey of different localization and NLOS mitigation algorithms is not available in the literature. This paper aims to give a comprehensive review of these different TOA-based localization algorithms and their technical challenges, and to point out possible future research directions. Firstly, fundamental lower bounds and some practical estimators that achieve close to these bounds are summarized for line-of-sight (LOS) scenarios. Then, after giving the fundamental lower bounds for NLOS systems, different NLOS mitigation techniques are classified and summarized. Simulation results are also provided in order to compare the performance of various techniques. Finally, a table that summarizes the key characteristics of the investigated techniques is provided to conclude the paper.     

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。      

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。      

紫外光散射通信接收技术研究

介绍了紫外光散射通信系统的基本原理和结构,分析了紫外光信号的传输特性,从光学系统、紫外滤光片和紫外探测器三个方面讨论了紫外光散射通信接收的相关技术.重点分析了紫外光散射通信对滤光片和紫外探测器的特性要求,为器件的选择研制提出了一定的依据,进一步探讨了紫外探测器的发展趋势.     

基于ToA和误差抑制的大规模网络节点定位

针对混合视距/非视距环境中的移动节点定位,提出一种基于到达时间测量值和误差抑制的定位方案。首先,配备有超宽带无线电的节点随机移动,以收集到达时间测量数据,并执行最短路径距离选择算法得到包括一跳节点距离在内的非视距误差减小后的多跳节点距离;采用多维标度确定节点的初始位置;采用迭代三边测量法和误差积累管理相结合来获得定位节点位置。仿真实验结果表明,提出的定位方案的定位精确度优于其他几种常用方案。