室内超宽带测距定位及NLOS抑制方法

针对UWB定位过程中易受NLOS等误差影响导致定位精度和稳定性下降,并且现有的NLOS识别剔除方法模型复杂、环境依赖性强等问题,本文提出一种信道特征二元假设和基于滑窗改进的UWB自主完好性监测相结合的误差识别剔除方法。该方法首先通过信道特征二元假设对粗差、部分NLOS误差识别剔除,在提升数据质量的同时能够最大限度地降低UWB定位自主完好性监测的单测距值故障假设的局限性;然后针对量级较小的NLOS误差,仍然存在识别不敏感问题,因此,结合滑窗改进的UWB定位自主完好性监测方法;最后在UWB定位自主完好性监测方法的基础上引入测距精度提升监测性能,并结合滑窗理论降低漏检情况,能够针对信道特征二元假设方法未识别的NLOS测距值进行有效识别剔除,实现对NLOS误差的进一步识别剔除。试验结果表明,本文方法能够对NLOS等误差进行有效识别剔除,提高UWB定位精度和可靠性。

基于ISODATA改进K均值聚类算法的NLOS识别技术

针对超宽带信号中非视距误差造成定位系统定位偏差的问题,提出了一种改进无监督算法的NLOS识别技术。本文提取信道脉冲冲激响应波形的8种特征参量,选择主成分分析算法对多维特征进行降维处理;采用基于迭代自组织数据分析法改进的K均值聚类算法,自适应地选择K值来区分视距和非视距信号;最后,结合特征参量的冗余性、相关性对分类结果进行判别。实验结果表明,该方法能有效地识别出NLOS信号,且具有较好的环境适应性,识别准确度达到95%。

基于CNN-CAM的NLoS/LoS识别方法研究

针对目前基于信道脉冲响应(Channel Impulse Response,CIR)的非视距(None Line of Sight,NLoS)/视距(Line of Sight,LoS)识别方法精度低、泛化能力差的问题,提出了一种多层卷积神经网络(Convolutional Neural Network,CNN)与通道注意力模块(Channel Attention Module,CAM)相结合的NLoS/LoS识别方法。在多层CNN中嵌入CAM提取原始CIR的时域数据特征,利用全局平均池化层代替全连接层进行特征整合并分类输出。使用欧洲地平线2020计划项目eWINE公开的数据集进行不同结构模型和不同识别方法的对比实验,结果表明,所提出的CNN-CAM模型LoS和NLoS召回率分别达到了92.29%与87.71%,准确率达到了90.00%,F1分数达到了90.22%。与现有多种传统识别方法相比,均具有更好的识别效果。

Consistent and Accurate TDOA Localization in Dynamic NLOS Environments via Prior Information Independent Estimation

In the time-difference-of-arrival(TDOA)localization,robust least squares(LS)problems solved by mathematical programming were proven to be superior in mitigating the effects of non-line-of-sight(NLOS)propagation.However,the existing algorithms still suffer from two disadvantages:1)The algorithms strongly depend on prior information;2)The approaches do not satisfy the mean square error(MSE)optimal criterion of the measurement noise.To tackle the troubles,we first formulate an MSE minimization model for measurement noise by taking the source and the NLOS biases as variables.To obtain stable solutions,we introduce a penalty function to avoid abnormal estimates.We further tackle the nonconvex locating problem with semidefinite relaxation techniques.Finally,we incorporate mixed constraints and variable information to improve the estimation accuracy.Simulations and experiments show that the proposed method achieves consistent performance and good accuracy in dynamic NLOS environments.

Optimal AP Deployment in Cell-Free Massive MIMO Systems with LoS/NLoS Transmissions:A Stochastic Geometry Approach

Cell-free massive multiple-input multipleoutput(MIMO)is a promising technology for future wireless communications,where a large number of distributed access points(APs)simultaneously serve all users over the same time-frequency resources.Since users and APs may locate close to each other,the line-of-sight(Lo S)transmission occurs more frequently in cell-free massive MIMO systems.Hence,in this paper,we investigate the cell-free massive MIMO system with Lo S and non-line-of-sight(NLo S)transmissions,where APs and users are both distributed according to Poisson point process.Using tools from stochastic geometry,we derive a tight lower bound for the user downlink achievable rate and we further obtain the energy efficiency(EE)by considering the power consumption on downlink payload transmissions and circuitry dissipation.Based on the analysis,the optimal AP density and AP antenna number that maximize the EE are obtained.It is found that compared with the previous work that only considers NLo S transmissions,the actual optimal AP density should be much smaller,and the maximized EE is actually much higher.

Passive IoT Localization Technology Based on SD-PDOA in NLOS and Multi-Path Environments

Addressing the challenges of passive Radio Frequency Identification(RFID)indoor localization technology in Non-Line-of-Sight(NLoS)and multipath environments,this paper presents an innovative approach by introducing a combined technology integrating an improved Kalman Filter with Space Domain Phase Difference of Arrival(SD-PDOA)and Received Signal Strength Indicator(RSSI).This methodology utilizes the distinct channel characteristics in multipath and NLoS contexts to effectively filter out interference and accurately extract localization information,thereby facilitating high precision and stability in passive RFID localization.The efficacy of this approach is demonstrated through detailed simulations and empirical tests conducted on a custom-built experimental platform consisting of passive RFID tags and an R420 reader.The findings are significant:in NLoS conditions,the four-antenna localization system achieved a notable localization accuracy of 0.25 m at a distance of 5 m.In complex multipath environments,this system achieved a localization accuracy of approximately 0.5 m at a distance of 5 m.When compared to conventional passive localization methods,our proposed solution exhibits a substantial improvement in indoor localization accuracy under NLoS and multipath conditions.This research provides a robust and effective technical solution for high-precision passive indoor localization in the Internet of Things(IoT)system,marking a significant advancement in the field.

NLOS环境下基于UWB的定位算法研究

为解决室内非视距(NLOS)环境下定位及机器人定位过程中由于非视距环境下,噪声存在异常值误差过大的问题,提出了两种基于Chan-Taylor协同算法来抑制NLOS误差的方案。一种为结合了卡尔曼滤波的抑制方案,另一种为依据测量特征统计的距离重构方案,从多种定位算法组合出发,有效避免了高噪声和NLOS误差的影响,并通过动静态定位测试分析两种方案的定位精度。结果表明,两种方案均能降低NLOS环境下的定位误差,但NLOS误差抑制算法的效果较优,适应性更强。

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。

NLOS环境下多传感器融合的室内定位方法

为提高超宽带(ultra-wideband,UWB)技术在非视距(non line of sight,NLOS)环境下的定位精度,提出一种基于粒子滤波融合视觉与UWB数据的定位方法。视觉模块通过识别与检测标签推算出绝对位姿;UWB模块鉴别由NLOS条件干扰的测距值,筛选最优测距值进行自适应权重的定位算法,提升覆盖区域的整体定位精度;基于粒子滤波将两者的实时定位信息进行数据融合。实验结果表明,融合定位方法具有实时性和鲁棒性,有效抑制了NLOS环境引起的误差,在NLOS环境下定位精度能够达到0.26 m。

基于两步Kalman滤波的NLoS误差抑制算法

针对超宽带(ultra wide band,UWB)定位中影响定位精度的非视距(non line of sight,NLoS)传播误差问题,提出了一种基于Kalman滤波的NLoS误差二次消除方法.该方法利用NLoS误差与测量误差之间的相互独立性,借助Kalman滤波将NLoS误差从总误差中单独分离出来,对其进行实时估计,并将该NLoS误差估计值作为NLoS误差辨别及测距值修正的依据.通过Kalman滤波对到达时间(time of arrival,TOA)测距值进行二次估计、鉴别及修正以提高TOA测距精度,从而实现室内复杂环境下的UWB精准实时定位.仿真实验结果表明:该方法不仅能够对NLoS误差实现良好的跟踪估计,对视距(line of sight,LoS)/NLoS环境转变也具有较强的灵敏感知能力,同时NLoS误差测距值在应用该方法后的定位性能逼近于LoS环境下的理想状态.

High-Accuracy NLOS Identification Based on Random Forest and High-Precision Positioning on 60 GHz Millimeter Wave

60 GHz millimeter wave(mmWave)system provides extremely high time resolution and multipath components(MPC)separation and has great potential to achieve high precision in the indoor positioning.However,the ranging data is often contaminated by non-line-of-sight(NLOS)transmission.First,six features of 60GHz mm Wave signal under LOS and NLOS conditions are evaluated.Next,a classifier constructed by random forest(RF)algorithm is used to identify line-of-sight(LOS)or NLOS channel.The identification mechanism has excellent generalization performance and the classification accuracy is over 97%.Finally,based on the identification results,a residual weighted least squares positioning method is proposed.All ranging information including that under NLOS channels is fully utilized,positioning failure caused by insufficient LOS links can be avoided.Compared with the conventional least squares approach,the positioning error of the proposed algorithm is reduced by 49%.

基于NLOS下混合滤波加权组合定位算法的仿真

超宽带(Ultra Wide Band, UWB)技术是一种新兴的无线载波通信技术,其具有发射信号功率谱密度低、系统复杂度低、定位精度高等优势,尤其适用于像电厂等密集多径场所的高速无线连接,但在传输过程中信号会被环境中的各种因素影响,进而会影响室内定位的精度。基于此,针对室内非视距(Non Line of Sight, NLOS)环境下,提出一种非视距混合滤波加权算法,能够有效对测距数据进行平滑处理,进而降低异常值的影响,再利用时间差定位法(Time Difference of Arrival,TDOA)测量方法,在原始Chan算法的基础上提出一种改进的Chan定位算法,解决NLOS误差引起的定位信息不准确的问题,最终实现更精准的TDOA定位。仿真实验证明,所提算法在室内NLOS环境中具有更高的定位精度。

基于改进高斯-牛顿法的NLOS误差消除三维定位模型

针对无线传感网络进行室内定位过程中由于信号非视距(NLOS)传播导致精度低的问题,提出一种基于改进高斯-牛顿法的NLOS误差消除室内三维定位模型.该模型基于欧式距离,利用最小二乘算法得到目标位置的初始解,并根据改进的高斯-牛顿法对非线性最小二乘估计值进行迭代,进一步降低NLOS误差的影响,收敛得到最终的精确位置.实验结果表明:该模型在三维空间的定位误差约在0.64 m,最大定位误差不超过1.29 m,误差小于1.2m的概率为96.5%,较其他定位方法有更好地定位效果.

NLOS非视距微波技术在通信保障中的研究应用

为满足城区或乡村改造等场景下的应急通信保障需求,降低城区或乡村区域布局结构调整对移动通信网络产生的影响,建立基于非视距微波技术快速恢复通信网络的支撑手段,为现阶段光缆中断时快速恢复通信提供帮助。

UWB定位中基于XGBoost的NLOS识别方法

NLOS场景下UWB室内定位精度下降,识别出NLOS有助于进一步提升定位精度。针对超宽带定位中非视距识别的问题,提出基于XGBoost的NLOS识别方法。基于XGBoost决策树算法,采用两个层面的数据源对UWB定位中NLOS进行识别:元数据层面,用真实定位场景中采集的UWB CIR数据作为数据源;统计数据层面,将CIR数据经过一定的加工,得到几个关键的信号特征参数作为数据源。两组实验结果表明,XGBoost算法在元数据和统计数据上识别精确度分别达到91%和92%。UWB定位中XGBoost算法能显著提高识别NLOS信号的准确度。

Research on the Performance of Non-Line-of-Sight Ultraviolet Communication in Rain and Fog Environment

Wireless ultraviolet communication is a new type of communication mode. It refers to the transmission of information through the scattering of ultraviolet light by atmospheric particles and aerosol particles. The scattering characteristics can enable the wireless ultraviolet communication system to transmit ultraviolet light signals in a non-line-of-sight manner, which overcomes the weakness that other free space optical communications must work in a line-of-sight manner. Based on the basic theory of scattering and absorption in atmospheric optics, taking the ultraviolet light with a wavelength of 266 nm as an example, this paper introduces the classical model of non-line-of-sight single-scattering coplanarity based on the ellipsoid coordinate system. The model is used to simulate and analyze the relationship between the geometric parameters such as transmission distance, transceiver elevation angle and transceiver half-angle and the received optical power per unit area. The performance of non-line-of-sight ultraviolet communication system in rain and fog environment is discussed respectively. The results show that the transmission quality of non-line-of-sight ultraviolet atmospheric propagation is greatly affected by the communication distance. As the distance increases, the received light power per unit area gradually decreases. In addition, increasing the emission elevation angle, the receiving elevation angle and the receiving half angle is an important way to improve the system performance.

基于改进卡尔曼滤波的NLOS误差消除算法

随着无线传感器的网络的日益发展,TOF测距的应用也越来越广。针对基于TOF测距的测量值中包含的由信号的非视距传播引起的误差,提出了自适应测量噪声和有色噪声卡尔曼滤波相结合的消除NL0S误差的算法。该算法首先对测量噪声建立有色噪声模型,滤波参数根据NL0S环境的恶劣情况动态调整,再结合卡尔曼滤波算法,进而实时调整滤波过程以获得最优估计。通过仿真实验表明,该算法误差小,精确度高,适合用于动态实时定位系统。

一种基于TDOA重构的蜂窝网定位服务NLOS误差消除方法(英文)

在非视距传播 (NLOS)环境中 ,到达时间差 (TDOA)测量值受NLOS误差的影响而产生偏差[1] ,使得Chan算法[2 ] 的定位性能显著下降。基于NLOS误差的先验信息 ,利用平均超量时延和均方根时延扩展之间的关系 ,本文为基于移动台 (MS)的定位方案提出了一种建立在TDOA测量值重构和Chan算法基础上的改进算法 ,使得平均超量时延的影响显著下降。分析和仿真结果表明 ,与Chan算法相比 。

IR-UWB测距中NLOS状态的鉴别

NLOS效应是IR-UWB测距定位系统中影响精度的主要因素之一。该文提出不依赖于信道估计,直接从接收信号采样序列中提取特征参量进行NLOS鉴别的方法,构造了采样序列的首均比与峰均比乘积作为鉴别参量,并与使用峭度和平均附加时延这两个参量的鉴别性能进行了对比。仿真结果表明:相干测距所对应的鉴别性能要好于非相干测距,原因是更为精确的DP检测结果及高采样速率使得参量提取值更能准确地反映信道状态信息;使用峭度和平均附加时延作为鉴别参量所能获得鉴别性能均不理想,而使用采样序列的首均比与峰均比乘积能使鉴别成功率提高10%左右。在定位模块中融入该文所获得的NLOS的鉴别结果将有助于定位精度的进一步提升。

基于NLOS环境下的TOA/AOA混合定位算法

非视距传播(NLOS)误差是影响各种蜂窝网络定位精度的主要原因.通过计算波达时间(TOA)和波达角(AOA)相对于移动台(MS)参考位置的残差来对NLOS误差进行鉴别,找出含有NLOS的基站.然后利用所得到的关于NLOS的信息,对算法进行加权处理.仿真实验表明,该算法有效的降低了NLOS的影响,可以得到更好的位置估计结果.