基于CIR特征参量的NLOS识别方法研究

多径干扰是超宽带(ultra-wideband,UWB)定位误差的主要来源之一,超宽带信号的非视距传播会导致通信和定位精度的可靠性降低。因此,准确识别定位过程中的非视距(non line of sight,NLOS)传播信号是提高定位精度的重要措施。针对超宽带信号的非视距传播识别问题,该文提出一种新的基于信道冲激响应(channel impulse response,CIR)特征参量—上升时间与峰值时间和(sum of rise time and peak time,Sum_T)与未检测到峰值(undetected peak,UD-P)联合的NLOS识别方法。实验结果表明,典型室内办公环境下NLOS信号的识别率可以达到95.75%,该方法在定位系统中的使用将有助于提升定位精度。

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CNN的NLOS识别与定位技术研究

超宽带(Ultra Wide Band,UWB)信号具有极高的时间分辨率,测距精度高、穿透能力强、抗多径效应好,适用于高精度室内定位系统的设计。基于UWB的室内定位系统中,非视距传播对定位精度有重要影响。针对UWB信号室内传播存在视距(Line of Sight,LOS)和非视距(Non Line of Sight,NLOS)情形,采用卷积神经网络(Convolutional Neural Network,CNN)识别NLOS信号。为避免有限数据集导致过拟合,采用dropout降低神经元之间的依赖,提高NLOS信号识别率。完成NLOS识别后使用LS/WLS算法做定位,结果表明该方法能将定位误差降低一半,显著提高了定位精度。

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.

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.

基于加权K-近邻分类的非视距识别方法研究

超宽带(UWB)定位系统中,针对复杂的环境下,信号的遮挡、直达信号的错误判断严重影响定位精度问题,该文基于信道冲激响应(CIR)提出一种新型特征参量——饱和度(S),结合前人提出的特征参量利用Relief算法和互信息特征选择(MIFS)算法进行特征选择,在相关性的基础上赋予特征相应的权重,选择最优的特征子集进行加权K-近邻(WKNN)分类,提高了非视距(NLOS)识别系统准确度。并且分析了WKNN算法中的训练数据集数量与近邻数K对算法的影响,确定优选方案,减小了算法计算量,提高了NLOS识别系统实时性。在不同环境下进行实验验证,结果表明,该方法具备较高的识别准确度和环境适用性,识别精度达到95%。

基于PCA-FCM的UWB非视距识别算法

近年来,室内定位技术得到了广泛的应用,超宽带(Ultra-wideband,UWB)凭借其独特的优势,在室内定位领域脱颖而出。但是在复杂的室内环境中,信号传播易受到非视距(Non Line of Sight,NLOS)障碍物遮挡,产生NLOS误差。针对传统NLOS障碍物识别方法,识别率低以及需要具体考虑应用场景等问题,提出了一种基于PCA-FCM的模糊聚类的识别方法,达到92%的识别率。首先,根据视距(Line of Sight,LOS)信号与非视距信号之间的差异,选取了信号强度、平均过量时延等6个信道特征参数;其次,鉴于主成分分析(Principal Components Analysis,PCA)模型在特征提取方面的优势,用PCA模型对特征参数进行降维处理;最后,用模糊C均值(Fuzzy C-Means,FCM)算法对降维之后的目标函数进行优化,得到聚类中心的隶属度,从而提高NLOS信号识别率。

基于卷积神经网络的OFDM-UWB信道环境识别

超宽带(UWB)无线通信技术被广泛应用于室内定位领域,其能识别出多径信道中的非视距信道,有助于去除影响信号的非理想因素,提升定位精度。基于OFDM方案的UWB系统,提出一种利用卷积神经网络对信道估计出的信道冲激响应时频图像进行训练,从而识别出信道环境的方法,将信道识别问题转化为图像识别问题,同时分析时频处理参数对识别结果的影响。仿真结果表明,该方法的识别率随通信系统比特信噪比(EbN0)的增加而提升,当EbN0增加至20 dB时稳定在90%,与传统基于支持向量机的信道识别方法相比获得了10%的性能提升。

无线电监测中的信号处理技术分析

文章概述了无线电监测的内涵和具体内容,详细论述在无线电监测中进行信号处理的关键技术,对无线电监测技术的发展和信号处理方法进行总结,为无线电监测中信号处理技术的发展提供理论参考。

基于CSI非视距识别的被动式指纹室内定位

由于基于WiFi的室内定位技术的主要挑战是多路径和非视距(NLOS),因此,室内定位可以从NLOS识别中获益。然而,来自商用WiFi的接收信号强度信息(RSSI),使得非视距识别受限于有限的带宽和粗略的多径分辨率。文章提出了一种更加细粒度的方法,即利用物理层的信道状态信息(CSI)进行室内非视距识别。利用网卡及现有的WiFi设备,采集室内环境的CSI信息并提取特征信息,构建视距和非视距CSI指纹。利用一种基于深度学习的神经网络算法进行非视距识别,从而达到室内定位的目的。在不同室内环境下的实验结果表明,本方案的NLOS识别率达到96.43%,能有效并准确地区分视距与非视距位置。

基于UWB和IMU的电力巡检机器人定位方法

为了满足日益增长的电力需求,我国建设了大量发电站和电能转换设施。长时间的负载运行使电力设备运行的不稳定性增加,一般需要通过人工巡检的方式进行检测和维护,但传统的人工巡检方法存在着较大的安全风险,在精度方面满足不了需求,维护效率也比较低。随着人工智能技术的发展,智能巡检机器人的出现给这一问题带来希望,它克服了传统人工巡检方式存在的弊端,但目前的巡检机器人在复杂电力环境中仍存在空间定位不准确等问题。为此,本文提出了一种将超宽带(UWB)和惯性传感器(IMU)以无迹卡尔曼滤波(UKF)进行融合的定位方法,该方法克服了UWB定位方法在有障碍物的非视距情况下定位误差大的问题,改进了复杂电力环境下智能巡检机器人的定位精度。

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.

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.

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。

非视距环境下的UWB/INS室内定位方法

针对UWB在室内定位的应用中会受到非视距环境影响导致定位性能减弱甚至丧失的问题,提出在使用功率增益判决结合概率统计方法识别非视距信号后,开展基于卡尔曼滤波的UWB/INS组合定位;通过在非视距环境下使用INS系统解算UWB位置信息,实现非视距室内定位。仿真实验结果表明,使用拓展卡尔曼滤波得到的X、Y、Z三轴定位误差可控制在0.5 m内但定位性能不稳定;使用无迹卡尔曼滤波处理得到的X、Y、Z三轴定位误差可以控制在0.3 m内且定位性能稳定。该结果为非视距环境下的UWB室内定位提供了技术支持。