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.

基于散射区模型与参数聚类的室外NLOS单站定位算法

室外非视距(non line of sight,NLOS)环境中障碍物会阻碍终端电磁波信号直接传播到基站,而且该条件下单基站收集的定位信息不足,导致定位精度不高。针对上述问题,提出一种基于空间布局的散射区模型和基于参数聚类的定位算法。根据固定基站附近的空间布局确定散射区并构建散射区模型,该算法基于该模型收集多径信号的测量参数,将k-means聚类(k-means clustering)和均值漂移聚类(means shift clustering)算法有效结合对参数聚类处理,再根据聚类结果和单站定位系统的几何结构建立方程组,将方程组的求解问题转化为非线性优化问题并利用列文伯格-马夸尔特(Levenberg-Marquardt,LM)算法求解优化问题估计目标位置。仿真结果表明,在室外NLOS环境中,且仅提供单个基站的条件下,该算法可以有效提高定位结果的精度。

NBP-based localization algorithm for wireless sensor networks in NLOS environments

To mitigate the impacts of non-line-of-sight（NLOS） errors on location accuracy, a non-parametric belief propagation（NBP）-based localization algorithm in the NLOS environment for wireless sensor networks is proposed.According to the amount of prior information known about the probabilities and distribution parameters of the NLOS error distribution, three different cases of the maximum a posterior（MAP） localization problems are introduced. The first case is the idealized case, i. e., the range measurements in the NLOS conditions and the corresponding distribution parameters of the NLOS errors are known. The probability of a communication of a pair of nodes in the NLOS conditions and the corresponding distribution parameters of the NLOS errors are known in the second case. The third case is the worst case, in which only knowledge about noise measurement power is obtained. The proposed algorithm is compared with the maximum likelihood-simulated annealing（ML-SA）-based localization algorithm. Simulation results demonstrate that the proposed algorithm provides good location accuracy and considerably outperforms the ML-SA-based localization algorithm for every case. The root mean square error（RMSE）of the location estimate of the NBP-based localization algorithm is reduced by about 1. 6 m in Case 1, 1. 8 m in Case 2 and 2. 3 m in Case 3 compared with the ML-SA-based localization algorithm. Therefore, in the NLOS environments,the localization algorithms can obtain the location estimates with high accuracy by using the NBP method.

基于毫米波雷达的非视距目标探测和定位方法研究

隐蔽非直视目标探测和定位技术在城市巷战、安防、防暴等领域具有广泛的应用。本文针对L形非视距区域内单目标的探测与定位问题进行研究。首先,对雷达布设位置及可探测区域进行了分析;其次,针对共同可探测区域,设计了多路径联合检测器;最后,基于设计的多径雷达应用系统,利用所提检测定位方法进行实验验证。与现有研究相比,本文的创新点在于:在L形非视距场景下,对宽波束毫米波雷达布设位置及可探测区域进行分析并给出合理建议;其次利用所提多路径联合检测器将不同反射面的多条路径联合使用,提高了检测性能。本文通过实验验证了系统的可靠性及雷达位置对可探测区域的影响,通过仿真与实验探究了所提检测器的性能改善。

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。

Passive location estimation using scatterer information for non-line-of-sight environments

In order to improve the performance of the traditional hybrid time-of-arrival（TOA）/angle-of-arrival（AOA）location algorithm in non-line-of-sight（NLOS）environments,a new hybrid TOA/AOA location estimation algorithm by utilizing scatterer information is proposed.The linearized region of the mobile station（MS）is obtained according to the base station（BS）coordinates and the TOA measurements.The candidate points（CPs）of the MS are generated from this region.Then,using the measured TOA and AOA measurements,the radius of each scatterer is computed.Compared with the prior scatterer information,true CPs are obtained among all the CPs.The adaptive fuzzy clustering（AFC）technology is adopted to estimate the position of the MS with true CPs.Finally,simulations are conducted to evaluate the performance of the algorithm.The results demonstrate that the proposed location algorithm can significantly mitigate the NLOS effect and efficiently estimate the MS position.

基于多径模型的建筑区SAR三维成像中非视距目标重定位方法

随着合成孔径雷达(SAR)系统小型化技术、SAR三维成像技术的发展,当前已可利用无人机载小型化阵列干涉SAR实现城区三维成像,在城市测绘、复杂环境重建等领域具有重要应用前景。然而,SAR进行城市场景三维成像时,回波中存在多径信号,会导致成像结果解译困难,却也给非视距区域隐蔽目标的发现提供了重要手段。为此,该文针对低空无人机载阵列干涉SAR建筑区三维成像中的非视距目标进行了研究,建立了非视距目标在低空阵列干涉三维成像下的多径模型,给出了城市峡谷区域利用多径扩大可视范围的计算方法,并基于建筑平面拟合提出了非视距目标重定位方法。无人机载阵列干涉SAR的仿真和实际数据处理验证表明,所提出的方法可以对城市峡谷非视距目标进行有效的三维成像和重定位,重定位误差小于0.5 m,实现了对非视距区域信息的获取。

基于非视距雷达三维成像的隐藏目标精确重构方法

非视距(NLOS)三维成像雷达是一种利用多径散射回波探测隐藏目标的新技术,但存在多路径回波分离、孔径遮蔽缩减、反射面相位误差等问题,传统视距雷达三维成像方法难以实现非视距隐藏目标的高精度成像。为此,该文提出了一种基于迭代稀疏重构的非视距隐藏目标三维成像雷达精确成像方法(NSIR)。在该方法中,首先构建非视距毫米波雷达三维成像的多径信号模型,利用视距/非视距回波特性,通过模型驱动方法提取非视距隐藏目标的多路径回波,实现视距/非视距回波信号的分离;其次,构建耦合多径反射面相位误差的全变分多约束隐藏目标重构优化问题,利用分裂Bregman全变分(TV)正则化算子和最小均方误差的相位误差估计准则,联合求解多约束最优化问题,实现非视距目标的精确成像及轮廓重构。最后,搭建平面扫描的三维成像雷达试验平台,完成了拐角非视距场景下刀具、铁架等目标的实验验证,验证了非视距毫米波三维成像雷达隐匿目标探测能力及该文方法的有效性。

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.

基于多级中继的无人机多链路定位研究

无人控制器是当今世界先进科技发展的热点载体,本文针对无人机超视距定位这一难点,提出了一种新的基于多级中继的无人机多链路定位方法。重点描述了无人机多级中继的选择和该方法与捷联惯导系统的组合,并且对该定位方法在无人机上的应用提出了建议方案。

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。

室内环境下基于虚拟传感器的TOA定位算法

室内条件下无线信号传输普遍存在非视距和多径问题,影响定位精度,特别是对基于波达时间(TOA)的几何定位方法影响更加严重。为解决上述问题,提出一种虚拟传感器两步加权最小二乘算法。在室内布局已知的条件下,利用虚拟传感器建立包括信号直射、反射、绕射和透射的TOA几何模型,针对每一种可能的信号传播情况进行位置估计,根据几何原理设立约束条件,确定目标位置。仿真结果表明,随着非视距程度的加剧,在视距条件下提出的算法精度明显优于其他非视距算法。

附加运动学约束的BDS抗差UKF导航算法

针对城市复杂环境中单一BDS导航受多路径(multipath,MP)和非视距(non-line-of-sight,NLOS)信号干扰导致精度下降的问题,提出一种附加运动学约束的抗差无迹卡尔曼滤波(unscented Kalman filter,UKF)算法。该算法基于新息向量构造等价权函数,克服了位置及接收机钟差初值不准确引起的抗差性能下降问题。同时,利用载体的近似运动方向和高程约束,进一步增强滤波解。实测车载试验结果表明,本文方法可有效抑制MP和NLOS信号的干扰,提高城市环境中的BDS导航精度。

复杂观测环境下GNSS信号分类及精度评估

基于高度角、信噪比和伪距残差3个指标,采用K均值(Kmeans^(++))、迭代自组织数据分析法(ISODATA)和基于密度带有噪声的空间分类法(DBSCAN)对复杂城市观测环境下的GNSS数据进行分类,并采用伪距单点定位模型(SPP)评估不同算法的分类精度。结果表明,Kmeans^(++)算法分类精度最优,在E、N、U 3个方向上的定位精度分别达2.56 m、3.25 m、9.73 m;相较于未采用Kmeans^(++)算法的定位精度分别提升57.86%、47.64%、60.98%。为进一步验证算法性能,将Kmeans^(++)算法与信噪比、高度角阈值法进行精度对比,结果表明,Kmeans^(++)算法的平面和三维定位精度均有显著改善,分别提升24.87%、39.07%(信噪比阈值法)和41.36%、59.91%(高度角阈值法)。

Non-Line-of-Sight Multipath Detection Method for BDS/GPS Fusion System Based on Deep Learning

Non-line-of-sight(NLOS)multipath effect is the main factor that restricts the application of global navigation satellite system(GNSS)in complex environments,especially in urban canyon.The effective avoidance of NLOS signals can significantly improve the positioning performance of GNSS receiver.In this paper,an NLOS/LOS classification model based on recurrent neural network is proposed to classify satellite signals received in urban canyon environments.The accuracy of classification is 91%,and the recognition rate of NLOS is 89%;the classification performance is better than that of traditional machine learning classification models such as support vector machine.For BeiDou navigation satellite system/global positioning system(BDS/GPS)fusion system,the least square algorithm and extended Kalman filter are used to estimate the position.The experimental results show that the three-dimensional positioning accuracy after NLOS recognition is improved about 60%on average compared with the traditional methods,and the positioning stability is also improved significantly.

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.

A robust vector tracking loop structure based on potential bias analysis

This paper proposes a robust vector tracking loop structure based on potential bias analysis. The influence of four kinds of biases on the existing two implementations of Vector Tracking Loops(VTLs) is illustrated by theoretical analysis and numerical simulations, and the following findings are obtained. Firstly, the initial user state bias leads to steady navigation solution bias in the relative VTL, while new measurements can eliminate it in the absolute VTL. Secondly, the initial code phase bias is transferred to the following navigation solutions in the relative VTL, while new measurements can eliminate it in the absolute VTL. Thirdly, the user state bias induced by erroneous navigation solution of VTLs can be eliminated by both of the two VTLs. Fourthly,the multipath/NLOS likely affects the two VTLs, and the induced tracking bias in the duration of the multipath/NLOS would decrease the performance of VTLs. Based on the above analysis,a robust VTL structure is proposed, where the absolute VTL is selected for its robustness to the two kinds of initialization biases;meanwhile, the instant bias detection and correction method is used to improve the performance of VTLs in the duration of the multipath/NLOS. Numerical simulations and experimental results verify the effectiveness of the proposed robust VTL structure.

毫米波雷达非视域目标检测

基于77 GHz调频连续波雷达,提出了一种在非视域环境下利用电磁波的多径效应从而实现人体目标检测的方法。首先,建立一个多径传播模型,并通过深入分析该模型,提出了一种有效的二维坐标点云生成方法。为了提高波达方向的测量精度,并满足实时性要求,提出了一种基于最小方差无畸变的改进方法来估计目标的波达方向,这一方法通过迭代求逆运算,显著地降低了运算复杂度。然后,针对多余的静止杂波,引入了主成分分析的杂波抑制手段,通过对回波数据的主成分分析,滤除了第一个主成分并重构数据,以达到杂波抑制的效果。最后,实验结果表明,所提方法可以准确地检测雷达视线外的目标。这一研究为未来的雷达技术应用提供了新的思路和技术支持,特别是在安全监控和自动驾驶等领域。

一种基于机器学习分类器的UWB异常信号识别方法

针对超宽带定位技术中非视距信号与多径信号影响超宽带定位精度的问题,在不考虑信道冲激响应这一重要特征前提下,分别从测试与训练场景相同和异同两方面对比研究支持向量机、随机森林和多层感知器3种机器学习方法的低成本超宽带异常信号识别性能。通过将多径信号与非视距信号分离,完成视距信号、非视距信号和多径信号的数据多分类。对比性实验结果表明,测试环境与训练环境相同和异同两种情况下的随机森林分类器表现最优,识别准确度可达92.52%、74.82%;在缺失信道冲激响应这一重要特征条件下,机器学习依然可以较好地识别非视距信号和多径信号,为低成本室内定位提供一种思路。