Structural Design of High-precision Positioning System in Weak Signal Environment Based on UWB and IMU Fusion

Aiming at the problem that indoor positioning technology based on wireless ultra-wideband pulse technology is susceptible to non-line-of-sight effects and multipath effects in confined spaces and weak signal environments,a high-precision positioning system based on UWB and IMU in a confined environment is designed.The STM32 chip is used as the main control,and the data information of IMU and UWB is fused by the fusion filtering algorithm.Finally,the real-time information of the positioning is transmitted to the host computer and the cloud.The experimental results show that the positioning accuracy and positioning stability of the system have been improved in the non-line-of-sight case of closed environment.The system has high positioning accuracy in a closed environment,and the components used are consumer-grade,which has strong practicability.

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NLOS环境下基于散射模型的TOA定位方法

减小非视距(Non Line Of Sight,NLOS)误差定位算法大多要求在移动台和基站之间至少存在一条视距(Line Of Sight,LOS)路径。提出一种新的NLOS环境中基于散射模型分类传播环境的TOA(Time Of Arrival)定位方法,将散射模型中NLOS传播的统计特性加入到定位算法中,使用散射模型研究了3种定位算法,方差匹配算法,期望最大算法和贝叶斯算法。并对算法进行仿真,仿真结果表明,本算法性能优于传统定位算法。

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.

LOS/NLOS混合环境中基于交互式多模型的鲁棒目标跟踪

针对视距(line-of-sight,LOS)和非视距(none-line-of-sight,NLOS)混合环境的定位跟踪问题,提出一种基于扩展H∞滤波(EHF)和扩展卡尔曼滤波(EKF)的交互式多模型(IMM)定位跟踪算法。目标与基站之间的LOS、NLOS传输信道分别用EKF和EHF匹配,模型间的转换用马尔可夫过程进行描述。Monte Carlo仿真结果表明,该算法具有较高的定位精度、较好的跟踪稳定性,且计算时间与基于EKF的IMM算法相当,具有较好的可实现性。

NLOS环境中基于二次修正的RSSI测距算法

RSSI测距实现简单,无需添加外部设备,但测距精度较低、易受环境影响。为了降低RSSI测距在NLOS环境中的误差,提出了一种基于二次修正的RSSI测距算法。首先用卡尔曼滤波对RSSI值进行预处理,然后采用最小二乘法拟合出传播方程,最后利用无迹卡尔曼滤波求出距离值。实验结果表明,该算法能降低NLOS环境中的测距误差,提高测距精度。

Novel Sum-of-Sinusoids Simulation Channel Modeling for 6G Multiple-Input Multiple-Output Vehicle-to-Everything Communications

In this paper,a statistical cluster-based simulation channel model with a finite number of sinusoids is proposed for depicting the multiple-input multiple-output(MIMO)communications in vehicleto-everything(V2X)environments.In the proposed sum-of-sinusoids(SoS)channel model,the waves that emerge from the transmitter undergo line-of-sight(LoS)and non-line-of-sight(NLoS)propagation to the receiver,which makes the model suitable for describing numerous V2X wireless communication scenarios for sixth-generation(6G).We derive expressions for the real and imaginary parts of the complex channel impulse response(CIR),which characterize the physical propagation characteristics of V2X wireless channels.The statistical properties of the real and imaginary parts of the complex CIRs,i.e.,autocorrelation functions(ACFs),Doppler power spectral densities(PSDs),cross-correlation functions(CCFs),and variances of ACFs and CCFs,are derived and discussed.Simulation results are generated and match those predicted by the underlying theory,demonstrating the accuracy of our derivation and analysis.The proposed framework and underlying theory arise as an efficient tool to investigate the statistical properties of 6G MIMO V2X communication systems.

多径利用雷达目标探测技术综述与展望

多径利用雷达(MER)目标探测技术主要基于电磁波在介质表面的反射、衍射等非直视(NLOS)多路径传播特性,实现对城市街角、车辆遮挡等“视觉”盲区内隐蔽目标的有效探测,其能够为城市作战、智能驾驶等多种应用提供服务,具有重要的现实意义和研究价值。为获知该领域的发展脉络,并预测未来可能的发展趋势,该文对21世纪初以来该领域国内外公开文献进行了归纳总结。相关文献的梳理结果表明,根据探测平台类型的不同,多径利用雷达目标探测技术目前主要包括两类:基于空中平台的多径探测技术和基于地面平台的多径探测技术。这两类技术均已取得一定具有实际意义的研究成果。针对空中平台,该文围绕可行性验证、影响因素分析、建筑环境感知和非视距目标探测4个方面展开梳理;针对地面平台,该文则从目标检测与识别、目标二维定位、目标三维信息获取及新型探测方法4个方面展开论述。最后,对多径利用雷达目标探测技术进行总结和展望,指出该技术在目前实际应用中所面临的潜在问题和挑战。这些结果表明,多径利用雷达目标探测技术正朝着多样化、智能化的方向发展。

A Robust Indoor Localization Algorithm Based on Polynomial Fitting and Gaussian Mixed Model

Wireless sensor network(WSN)positioning has a good effect on indoor positioning,so it has received extensive attention in the field of positioning.Non-line-of sight(NLOS)is a primary challenge in indoor complex environment.In this paper,a robust localization algorithm based on Gaussian mixture model and fitting polynomial is proposed to solve the problem of NLOS error.Firstly,fitting polynomials are used to predict the measured values.The residuals of predicted and measured values are clustered by Gaussian mixture model(GMM).The LOS probability and NLOS probability are calculated according to the clustering centers.The measured values are filtered by Kalman filter(KF),variable parameter unscented Kalman filter(VPUKF)and variable parameter particle filter(VPPF)in turn.The distance value processed by KF and VPUKF and the distance value processed by KF,VPUKF and VPPF are combined according to probability.Finally,the maximum likelihood method is used to calculate the position coordinate estimation.Through simulation comparison,the proposed algorithm has better positioning accuracy than several comparison algorithms in this paper.And it shows strong robustness in strong NLOS environment.

城市复杂环境下不同加权模型对动态SPP定位性能影响分析

城市复杂环境下,由于建筑物的遮挡,卫星定位严重受到非视距信号的影响。测量值随机误差加权模型是卫星定位的关键环节,它能够合理地分配各卫星在解算中所起作用的大小。因此,加权模型常用于缓解非视距信号对卫星定位的影响。本文针对目前主流的多种加权模型进行了系统总结和阐述,比较和分析了城市复杂环境下不同加权模型对卫星动态单点定位的影响。试验结果表明,各加权模型均能够在一定程度上提高定位精度,其中综合加权模型具有最优的性能表现。

Non-LOS target localization via millimeter-wave automotive radar

This paper considers the non-line-of-sight(NLOS)vehicle localization problem by using millimeter-wave(MMW)automotive radar.Several preliminary attempts for NLOS vehicle detection are carried out and achieve good results.Firstly,an electromagnetic(EM)wave NLOS multipath propagation model for vehicle scene is established.Subsequently,with the help of available multipath echoes,a complete NLOS vehicle localiza-tion algorithm is proposed.Finally,simulation and experimental results validate the effectiveness of the established EM wave propagation model and the proposed NLOS vehicle localization algorithm.

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%.

室内环境下基于IMM-EKF算法的移动目标定位

如何在视距(line-of-sight,LOS)与非视距(non-line-of-sight,NLOS)混合的室内环境中提高移动目标定位的精度,这是一项富有挑战性的工作.移动目标在室内环境移动时,其与传感器网络节点之间的信号传播在LOS与NLOS之间随机切换,导致移动节点定位精度下降.提出一种交互式多模型-扩展卡尔曼滤波(interactive multiple model-extended Kalman filter,IMM-EKF)定位算法.根据LOS?NLOS环境下不同的测距误差特性,在IMM框架中采用2个平行的卡尔曼滤波器(Kalman filter,KF)模型对测量距离同时进行滤波,根据滤波结果和测量值计算2个模型的似然概率,模型间的转换通过Markov链实现,2个KF滤波结果加权融合后获得IMM距离估计值.在EKF定位阶段,通过位置预测和更新估计出移动目标位置.仿真结果表明,IMM-EKF算法能够有效抑制NLOS对目标定位的影响,其定位精度优于单模型算法.

室内非视距环境中60GHz电波传播特性仿真与分析

毫米波波段,尤其是60 GHz频段已经被认为是最有前途的并且能够分配大量频谱空间的频段。文中基于SBR/Image方法对室内非视距环境中60 GHz电波传播进行了研究,仿真结果与已知文献中的测量结果一致性良好,验证了该方法的正确性。同时,对仿真60 GHz毫米波和2.4 GHz分米波两个不同频段所得到的接收功率和均方根时延扩展等参数进行了比较与分析。通过60 GHz频段与2.4 GHz频段仿真结果比较,两者的平均接收功率和平均RMS时延扩展有着显著的差异。仿真结果表明,60 GHz比较适合近距离小范围组网,并且在短距离通信上的安全性能与抗干扰性能方面存在一定的优势,该研究为以后的毫米波通信的室内网络分布与规划奠定了理论基础。

基于IMM-UKF-CS的混合环境目标跟踪

针对视距(Line of Sight,LOS)和非视距(None-Line of Sight,NLOS)混合环境机动目标跟踪问题,提出一种基于"当前"统计模型(current statistical,CS)和无迹卡尔曼滤波(unscented Kalman filter,UKF)的交互式多模型方法(IMM-UKF-CS)。该方法在交互式多模型的框架内,利用CS在机动目标跟踪方面的优势,并选择具有较高跟踪精度且计算代价较低的UKF作为子滤波器。仿真结果表明:在LOS/NLOS混合环境中,IMM-UKF-CS具有较高的跟踪精度、较强的鲁棒性及较低的时间代价,具有良好的应用价值。

一种新的非视距环境下移动台定位算法

非视距传播已经成为制约蜂窝网络定位的主要障碍。为了抑制和减少非视距传播引起的位置估计误差,提出了一种稳健估计的移动台定位算法———基于最大似然估计的位置估计算法,不需要先验NLOS误差统计特性和时序测量等前提条件,抑制NLOS误差的影响。通过跟传统的最小-均方算法(ordinaryleastsquare)比较,仿真结果表明这种算法有效地提高了在非视距环境下的定位精度。

城市典型环境下单频导航型GNSS接收机多路径误差特性分析

在高大建筑物、树木遮挡等城市复杂环境中,多路径误差和纯反射(NLOS)信号误差难以准确建模,严重影响行人GNSS导航定位的精度和可用性。本文首先对比分析了多路径延迟和纯反射信号延迟的误差特性,然后利用双差观测方程提取多路径和NLOS延迟误差,分析了两种延迟误差对伪距、载波相位和多普勒观测值的影响。通过UBLOX-NEO-M8T单频导航型接收机在建筑物附近和树木遮挡两种观测环境下的实测数据处理与分析,结果表明:载噪比(C/N0)较小时多普勒噪声明显变大。对于M8T导航型接收机,多路径影响严重的环境下C/N0低于30 d BHz,多普勒噪声变大。而NLOS影响严重的环境下多普勒噪声与C/N0之间关系不明显,在较大的C/N0,多普勒噪声仍然很大;在树木遮挡时,主要受多路径效应的影响,引起伪距的误差量级在10~150 m之间,对相位和多普勒的影响不明显;在高楼遮挡时,会出现NLOS信号干扰,对伪距和载波相位观测值的影响相同,延迟量大小与反射点的距离有关,可达到几百米,对多普勒观测值也有明显影响。

基于卷积神经网络的超宽带信道环境的分类算法

针对非视距(NLOS)状态鉴别需要已知信道类型的分类的问题,提出了一种基于卷积神经网络(CNN)的信道环境分类算法。首先,对超宽带(UWB)信道进行采样,构建样本集合;然后,利用样本集合训练CNN,对不同的信道场景特征进行提取;最终实现超宽带信道环境的分类。实验结果表明:所采用的分类方法的总模型准确率约为93.40%,能有效地实现信道环境的分类识别。

动态环境下UWB测距的NLOS影响分析与优化

针对动态环境下超宽带(Ultra Wide Band,UWB)测距易受到非视距(Not Line of Sight,NLOS)因素影响的问题,基于非对称双边双向测距(Asymmetric Double-Sided Two-Way Ranging,ADS-TWR)算法设计实验以探究常见NLOS因素(钢板、人体、木板)对UWB测距的影响。实验结果表明:NLOS对测距的影响程度不仅与遮挡物性质有关,还与其位置相关,在1.6m范围内越靠近UWB节点,NLOS误差越显著,超过1.6m后测距结果在一定程度上能够满足测距需求;在节点间距动态变化过程中出现NLOS因素时,测距输出值变化速率显著增大。根据分析所得动态NLOS误差特征,提出一种NLOS快速判断方法,设计自适应状态更新方程的卡尔曼滤波消减NLOS影响,经实验验证可提升50%以上的测距精度。

安卓智能手机GNSS数据筛选方案研究

通过设置两种不同的遮挡环境,对测地型接收机和智能手机静态采集的测量数据进行对比分析。结果表明,智能手机可接收部分非直射卫星信号,受多路径效应干扰明显。根据非直射卫星信号的数据特征设计智能手机原始GNSS数据筛选方案,静态定位实验结果表明,应用数据筛选方案能够显著提升智能手机在复杂环境下的定位精度。智能手机SPP定位算法和RTK定位算法平面方向的定位精度可提升20%~40%,高程方向的定位精度可提升30%~60%。