UWB positioning enhancement using Markov chain in indoor NLOS environment

The accuracy of the positioning system in indoor environment is often affected by none-line-of-sight ( NLOS) propagation. In order to improve the positioning accuracy in indoor NLOS environment, a method used ultra-wide-band ( UWB ) technology, which based on time of arrival ( TOA) principle, combining Markov chain and fingerprint matching was proposed. First, the TOA algorithm is used to locate the target tag. Then the Markov chain is used to identify if blocking happened and revise the position result. And the fingerprint matching is used to further improve the position accuracy. Finally, an experiment system was built to test the accuracy of the proposed method and the traditional Kalman filter method. The experimental results show that, compared with the traditional Kalman filter method, the proposed method can improve the positioning accuracy in indoor NLOS environment.     

卡尔曼滤波在iBeacon室内定位系统中的应用

为了抑制iBeacon信号在传播过程中由于人员扰动、多径效应等引起的噪声,以及移动定位过程中定位结果的不稳定,在iBeacon室内定位系统中引入了卡尔曼滤波算法分别对采集到的iBeacon信号与移动定位结果进行处理.首先通过卡尔曼滤波对采集的信号进行处理,降低了信号噪声,提高了信号的质量,其次应用卡尔曼滤波对移动定位结果进行二次处理,提高了移动定位结果的稳定性与精度.实验结果表明,在办公室环境下采用卡尔曼滤波进行处理后可将1 m以内的定位精度提高到80%以上.     

基于UWB和惯性导航融合的室内定位方法

针对室内环境日益复杂,单一的定位系统已经不能满足人们对定位准确度需求的问题,设计了一种利用超宽带(UWB)和惯性导航融合进行的室内定位方法:首先针对UWB测距结果容易受环境影响的问题,根据实验环境对UWB测距进行了标定;然后利用改进马氏距离的异常值检测方法对测距过程中的异常值进行了剔除;最后采用了紧耦合的卡尔曼滤波器,以UWB测距值作为扩展卡尔曼滤波观测量,以惯性导航解算的位姿作为扩展卡尔曼滤波器的预测量,通过UWB测距来不断校正惯性导航的位姿数据。最终为了验证所提方法的可行性和有效性,进行了UWB单独定位和UWB与惯性导航融合定位的小车搭载矩形运动轨迹实验,通过对两种方法实验数据的对比分析,在加入了外界干扰时的矩形轨迹定位实验中,利用UWB和惯性导航融合的定位结果,平均精度比单独利用UWB进行定位时提高了36.3%;误差结果对比表明,利用UWB和惯性导航融合定位的误差波动更小,具有更高的鲁棒性。表明了该融合定位算法与单独利用UWB技术进行定位的算法相比,能够有效地抑制定位过程中的干扰问题,并且可显著地提高在室内环境下定位系统的鲁棒性和定位精度。     

一种新型的INS与UWB融合的室内行人跟踪方法

针对单一室内定位系统定位精度低、鲁棒性差的问题,提出了一种新型的基于动态鲁棒容积卡尔曼滤波的超宽带(Ultra-wideband,UWB)与惯性导航系统(Inertial Navigation System,INS)融合的定位方法.首先建立了一种易于实现的UWB-INS融合定位框架,然后提出了一种动态鲁棒容积卡尔曼滤波算法以处理多源数据的融合.提出的滤波算法可将M估计理论、强跟踪算法、动态增强策略与传统的容积卡尔曼滤波算法结合,以此缓解外界噪声和系统模型误差对状态估计的不利影响.在UWB-INS组合定位框架内采用动态鲁棒容积卡尔曼滤波,可实现对室内行人运动轨迹的精确稳定跟踪.实际数据测试和Matlab仿真验证了所提方法在复杂环境下其定位精度和鲁棒性均优于单一依赖UWB或INS技术的定位系统.     

室内Wi-Fi/PDR自适应鲁棒卡尔曼滤波融合定位方法

针对室内复杂环境下信道状态信息的动态性问题,本文提出了一种面向室内Wi-Fi/行人航迹推算（Pedestrian Dead Reckoning,PDR）融合定位的自适应鲁棒卡尔曼滤波方法.该方法利用自适应鲁棒卡尔曼滤波将Wi-Fi传播模型与PDR定位信息进行多重融合,推算用户的最优估计位置.同时,基于滤波反馈机制,通过融合定位结果对加权最小二乘法中的路径损耗指数和滤波模型中的观测协方差进行动态修正,保证Wi-Fi传播模型接近于真实室内环境.实验结果表明,该方法能够有效解决室内复杂环境下单一Wi-Fi定位精度低和PDR累积误差的问题,此外,路径损耗指数和观测协方差的实时修正可以提高融合定位系统的定位精度和稳定性.     

一种超宽带与惯导融合的LSTM室内定位算法

针对复杂环境下的室内高精度定位需求,提出了一种超宽带和惯导融合定位方案。结合位置估计过程可被划分为时间序列预测问题的特点,提出了一种基于长短时记忆(Long Short Term Memory,LSTM)网络的联合定位算法,并对其总体架构设计、数据预处理方法、网络结构设计、模型训练方法进行了研究。在此基础上,通过仿真和实测实验对联合定位算法进行验证,实验结果表明,该LSTM神经网络联合定位算法的定位精度优于传统TOA(Time of Arrival)、UKF(Unscented Kalman Filter)联合定位算法,适用复杂室内定位。     

Indoor BLE and MEMS based multi-floor fusion positioning algorithm

Based on the data fusion from micro electro mechanical system (MEMS) sensors and low-power bluetooth (BLE),an indoor BLE and MEMS based multi-floor positioning algorithm was proposed.First of all,the affinity propagation clustering,outlier detection and received signal strength indicator (RSSI) filtering algorithms were applied to denoise the fingerprint database.Second,by using the extended Kalman filter,the robust M estimation algorithm was used to perform the optimal estimation of the two-dimensional target position.Finally,the barometer output and geographical position information was considered to realize the height estimation of the target.The experimental results show that the proposed system is able to achieve the horizontal and vertical positioning errors lower than 0.7 m and 0.35 m respectively in multi-floor fusion positioning.     

基于交互多模卡尔曼滤波器的定位算法"

针对蜂窝网中,由于电磁波的非视距传播导致定位精度较低的问题,将交互多模(IMM)方法引入到蜂窝网定位中。详细阐述了基于卡尔曼滤波器的交互多模算法(IMMKF)的工作原理以及IMMKF在蜂窝网定位中的应用,通过动态环境下的仿真试验验证了IMMKF能随信道传播环境的变化,在视距与非视距滤波模型之间进行自适应切换,达到抑制非视距误差的目的,并对IMMKF的滤波性能进行了分析和仿真验证。     

MIMO Channel Estimation and Tracking Based on Polynomial Prediction With Application to Equalization

This paper presents a multiple-input-multiple-output (MIMO) receiver design with integrated channel estimation and tracking for a time-varying frequency-selective Rician or Rayleigh fading environment. It first extends a polynomial-predictor-based channel estimation and tracking approach to a MIMO system. The structure and complexity of the estimator are similar to that of an optimum estimator using a Kalman filter, but it does not require a priori knowledge of the channel statistics. It employs a fixed-state transition matrix using precomputed polynomial coefficients and can be used in a Rician fading environment without reconfiguration. It is integrated with a MIMO minimum-mean-squared-error decision feedback equalizer, and simulation results show that the system performance using the estimator can be made comparable to that employing a Kalman estimator under a broad range of channel conditions.     

多距融合室内定位算法研究

目前WiFi在室内环境中使用频次高,用户在通过两个相邻AP时会发生AP切换,并累计产生大量WiFi访问日志。WiFi日志中包含定位所需的接收信号强度指示符,在定位系统中直接利用WiFi访问日志中的数据,将极大地简化定位部署复杂度。文中提出了一种在两个相邻AP环境下,基于WiFi日志的多距融合室内定位算法,并通过实验仿真将新算法与路径损耗模型定位方法进行对比。实验结果表明,新算法具有易部署、低成本、低复杂度等特点,当训练样本个数达到300时即可达到稳定定位效果。     

Robust range‐only beacon mapping in multipath environments

This study proposes a robust range‐only beacon mapping method for registering the locations of range‐only beacons automatically. The proposed method deals with the multipath propagation of signals from range‐only beacons using the range‐only measurement association (RoMA) and an unscented Kalman filter (UKF). The RoMA initially predicts the candidate positions of a range‐only beacon. The location of the range‐only beacon is then updated using the UKF. With the proposed method, the locations of range‐only beacons are accurately estimated in a multipath environment. The proposed method also provides the location uncertainty of each range‐only beacon. Simulation results using the model for multipath propagation and experimental results in a real indoor environment verify the performance of the proposed method.     

TRM-FMM室内无线定位技术

在室内多径环境下信号视距传播易受障碍物影响,导致现有的一些室内定位技术对室内环境分布的估计较为困难。时间反转镜( TRM)室内无线定位技术可以有效地减少室内多径效应对信号的影响以及复杂环境造成的延时。但是,若没有信号传输信道的信息,常规TRM技术的定位精度就会大打折扣。针对该问题,给出了一种基于快速行进算法( FMM)的TRM室内无线定位方法。该方法首先利用FMM和同时代数重建算法( SART )迭代更新计算室内环境分布,然后使用估计结果进行TRM定位。仿真结果显示,对于小型规模的目标物体定位误差约为1.84 cm,在未知室内信道信息的仿真环境下,该方法比常规TRM技术的定位精度提高约32.90倍。     

基于室内外定位的校园LBS系统设计与实现

为了解决目前智能手机中位置服务LBS(Location Based Service)类移动应用在小环境区域不能准确提供位置服务的问题,以校园为例提出了一种基于室内外定位的LBS系统设计方案。首先介绍系统结构设计,然后详细分析了Mobile GIS、GPS、基于Wi Fi信号强度值的位置指纹定位算法等系统设计中的关键技术,最后测试验证系统的各个功能模块。测试结果表明,系统可以实现终端定位、室内位置服务、校园导航、地图服务等功能,且操作便捷,具备可行性和实用性。     

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%.     

利用时变噪声贝叶斯卡尔曼滤波的室内移动目标定位

基于射频识别的指纹滤波定位技术是当前室内定位中常使用的技术之一。针对该技术存在的卡尔曼滤波算法不能准确适应环境噪声变化,致使定位精度不高的问题,提出了一种适应时变噪声的贝叶斯卡尔曼滤波算法。所提算法结合Sage-Husa滤波模型和贝叶斯模型,实现了过程和测量协方差矩阵的最优化,有效地降低了噪声,提高了指纹滤波定位的精度。实验结果表明,与变分贝叶斯卡尔曼滤波和Sage-Husa滤波相比,无障碍情况下,基于改进算法的定位精度提高了6%以上;有障碍干扰下,则提高了14. 6%以上。     

Modification of the nonlinear kalman filter in a correction scheme of aircraft navigation systems

An algorithm for correction of the navigation data of an aircraft that uses an inertial navigation system and the global positioning system (GPS) is considered. Estimation algorithms, namely, linear and nonlinear Kalman filters, are investigated. A method for modification of the nonlinear Kalman filter with the use of a genetic algorithm has been developed. To estimate the operability and accuracy of the modified nonlinear Kalman filter, simulation based on the data of a laboratory experiment conducted with the use of real navigation systems KIND34-059 and AIST-350 has been performed. The results of the laboratory experiments conducted with the use of real navigation systems have demonstrated high accuracy of data processing by the nonlinear Kalman filter modified by the genetic algorithm.     

基于仿射传播聚类的概率分布室内定位算法

随着定位技术的快速发展,基于无线局域网的室内定位成为新的研究热点。本文提出了一种基于近邻传播聚类的概率分布无线局域网(WLAN)室内定位算法。与传统室内定位算法相比,该算法首先引入近邻传播聚类缩小参考点搜索空间,然后利用概率分布定位算法进行精确定位。仿射传播聚类可以有效减少概率分布定位算法的计算量,应用于系统后将有效降低系统复杂度。实验结果表明,本文所提算法具有更好的定位精度,可实现对WLAN室内定位目标的快速、可靠定位。     

基于容错决策树的UWB辅助惯性定位方法

针对惯性导航系统误差随时间累积和超宽带(UWB)定位受到非视距问题、多径效应和人体影响出现粗大误差的问题,提出了一种基于容错决策树的UWB辅助人员室内惯性定位方法。该方法提出并采用陀螺仪高精度分段拟合误差补偿模型,抑制惯性导航误差漂移;同时在UWB辅助人员室内惯性定位的基础上,构建惯性导航与UWB单点定位数据共同作用的容错决策树判定模型,剔除UWB定位的粗大误差因子,进而对惯性导航和UWB的参数应用扩展卡尔曼滤波,实现UWB辅助增强惯性定位。根据实验验证表明,在复杂狭窄巷道环境,该方法将距离均方误差占路线长度的比例从6.02%提升到0.76%;在常规方正室内环境,该方法将最大误差占路线长度的比例从2.207%提升到0.635%。实现了长时间的连续可靠定位,具有较强的工程应用价值。     

Pedestrian Navigation Based on Inertial Sensors, Indoor Map, and WLAN Signals

As satellite signals, e.g. GPS, are severely degraded indoors or not available at all, other methods are needed for indoor positioning. In this paper, we propose methods for combining information from inertial sensors, indoor map, and WLAN signals for pedestrian indoor navigation. We present results of field tests where complementary extended Kalman filter was used to fuse together WLAN signal strengths and signals of an inertial sensor unit including one gyro and three-axis accelerometer. A particle filter was used to combine the inertial data with map information. The results show that both the map information and WLAN signals can be used to improve the pedestrian dead reckoning estimate based on inertial sensors. The results with different combinations of the available sensor information are compared.     

稀疏参考点下的室内定位方法

随着移动互联网的发展,人们对于室内的位置服务需求日益增加。基于Wi-Fi的指纹库室内定位算法具有成本低、定位误差小的优点,但指纹库信号采集需要消耗大量的时间和人力,本文对稀疏参考点下构建高效指纹数据库和高精度室内定位的方法进行了深入研究。本文改进了卡尔曼滤波有效解决了Wi-Fi的噪声和缺失点,设计了基于信号强度差分方差的无线接入点筛选策略来滤除信息量较低的接入点,提出了一种基于支持向量回归拟合的克里金插值算法（Kriging Interpolation Algorithm Based On Support Vector Regression, SVR-Kriging）进行指纹库的构建,最后通过接入点加权的K加权近邻法（AP weighted and Weighted K-Nearest Neighbor, AWKNN）完成定位。将该方法应用于实际的二维、三维定位场景,实验结果表明二维场景平均定位误差为1.01 m,三维场景平均定位误差为0.92 m。该方法解决了指纹数据库信号采集困难、接入点数据冗余的问题,有效地降低了定位误差。