Performance Evaluation of Three-Dimensional UWB Real-Time Locating Auto-Positioning System for Fire Rescue

Fire rescue challenges and solutions have evolved from straightfor-ward plane rescue to encompass 3D space due to the rise of high-rise city buildings.Hence,this study facilitates a system with quick and simplified on-site launching and generates real-time location data,enabling fire rescuers to arrive at the intended spot faster and correctly for effective and precise rescue.Auto-positioning with step-by-step instructions is proposed when launching the locating system,while no extra measuring instrument like Total Station(TS)is needed.Real-time location tracking is provided via a 3D space real-time locating system(RTLS)constructed using Ultra-wide Bandwidth technology(UWB),which requires electromagnetic waves to pass through concrete walls.A hybrid weighted least squares with a time difference of arrival(WLS/TDOA)positioning method is proposed to address real path-tracking issues in 3D space and to meet RTLS requirements for quick computing in real-world applications.The 3D WLS/TDOA algorithm is theoretically constructed with the Cramer-Rao lower bound(CRLB).The computing complexity is reduced to the lower bound for embedded hardware to directly compute the time differential of the arriving signals using the time-to-digital converter(TDC).The results of the experiments show that the errors are controlled when the positioning algorithm is applied in various complicated situations to fulfill the requirements of engineering applications.The statistical analysis of the data reveals that the proposed UWB RTLS auto-positioning system can track target tags with an accuracy of 0.20 m.

Real-time localization for underwater equipment using an extremely low frequency electric field

A new real-time underwater equipment location method adopting an electric field induced by a standard current source is proposed.Our goals were real-time tracking and location of stationary or moving underwater equipment both in shallow and deep seas,under noisy conditions.The main features of this method are as follows:(1)a standard current source on the water surface,which can be towed by a vehicle,consisting of two electrodes,a signal generator,and a GPS unit;(2)measurement of the extremely low frequency(ELF)electric field emitted by the current source,made possible by electric field sensors on the underwater equipment;(3)position of the underwater equipment is estimated in real time based on a progressive update extended Kalman filter(PUEKF),which is carried out using the propagation model of an ELF electric field because the electric field at the position of the underwater equipment and the current source position are known.We verified the accuracy of our method and confirmed real-time location feasibility through numerical,physical scale,and real-time sea experiments.Through numerical experiments,we verified that our method works for underwater equipment location in real-world conditions,and the location error can be less than 0.2 m.Next,real-time location experiments for stationary underwater measuring equipment in water tank were conducted.The result shows that the location error can be less than 0.1 m.We also confirmed real-time location feasibility through the use of offshore experiment.We expect that our method will complement conventional underwater acoustic location methods for underwater equipment in acoustically noisy environments.

Multilayer ANN indoor location system with area division in WLAN environment

An indoor location system based on multilayer artificial neural network（ANN） with area division is proposed.The characteristics of recorded signal strength（RSS）,or signal to noise ratio（SNR） from each available access points（APs）,are utilized to establish the radio map in the off-line phase.And in the on-line phase,the two or three dimensional coordinates of mobile terminals（MTs） are estimated according to the similarity between the new recorded RSS or SNR and fingerprints pre-stored in radio map.Although the feed-forward ANN with three layers is sufficient to describe any nonlinear mapping relationship between inputs and outputs with finite discontinuous points,the efficient inputs for better training performances are difficult to be determined because of complex and dynamic indoor environment.Then,the discussion of distance relativity for different signal characteristics and optimal strategies for multi-mode phenomenon avoidance is presented.And also,the feasibility and effectiveness of this method are verified based on the experimental comparison with normal ANN without area division,K-nearest neighbor（KNN） and probability methods in typical office environment.

RFID Complex Event Processing: Applications in Real-Time Locating System

Complex event processing (CEP) can extract meaningful events for real-time locating system (RTLS) applications. To identify complex event accurately in RTLS, we propose a new RFID complex event processing method GEEP, which is based on the timed automata (TA) theory. By devising RFID locating application into complex events, we model the timing diagram of RFID data streams based on the TA. We optimize the constraint of the event streams and propose a novel method to derive the constraint between objects, as well as the constraint between object and location. Experiments prove the proposed method reduces the cost of RFID complex event processing, and improves the efficiency of the RTLS.

Clustering Indoor Location Data for Social Distancing and Human Mobility to Combat COVID-19

The world is experiencing the unprecedented time of a pandemic caused by the coronavirus disease(i.e.,COVID-19).As a countermeasure,contact tracing and social distancing are essential to prevent the transmission of the virus,which can be achieved using indoor location analytics.Based on the indoor location analytics,the human mobility on a site can be monitored and planned to minimize human’s contact and enforce social distancing to contain the transmission of COVID-19.Given the indoor location data,the clustering can be applied to cluster spatial data,spatio-temporal data and movement behavior features for proximity detection or contact tracing applications.More specifically,we propose the Coherent Moving Cluster(CMC)algorithm for contact tracing,the density-based clustering(DBScan)algorithm for identification of hotspots and the trajectory clustering(TRACLUS)algorithm for clustering indoor trajectories.The feature extraction mechanism is then developed to extract useful and valuable features that can assist the proposed system to construct the network of users based on the similarity of the movement behaviors of the users.The network of users is used to model an optimization problem to manage the human mobility on a site.The objective function is formulated to minimize the probability of contact between the users and the optimization problem is solved using the proposed effective scheduling solution based on OR-Tools.The simulation results show that the proposed indoor location analytics system outperforms the existing clustering methods by about 30%in terms of accuracy of clustering trajectories.By adopting this system for human mobility management,the count of close contacts among the users within a confined area can be reduced by 80%in the scenario where all users are allowed to access the site.

Indoor Air Quality Real-Time Monitoring Results of Pathology Department

In this work, the objectives were to provide a scientific basis for environmental governance and to ensure staff health by real-time monitoring of indoor air quality of the pathology department. Using eagle eye environment monitor to make a real-time dynamic monitoring of the air quality of the pathological technical room for 30 days, the paper records the monitoring data of PM 2.5, PM 10, formaldehyde, CO2, total volatile organic compounds (TVOC) every day at Beijing time 3 a.m, 10 am, 1 pm, 4 pm, and 10 pm, and makes a summarization and analysis. The average value of CO2 concentration of the 5 time points is (0.05 ± 0.01)%, and each time point concentration are different (P 0.05);the average TVOC concentration of the 5 time points is (0.08 + 0.31) mg/m3. They are all different between the concentration at each time point (P 2 and NO are not checked out. Through the real-time online monitoring of the pathology room, we find that the formaldehyde concentration of different time periods is far more than the safety value standard, and the concentration of formaldehyde, CO2, PM 2.5, PM 10 and total volatile organic compound (TVOC) is different at different time periods, and the concentrations at working time are higher than the non-working time. We must take effective measures to control the concentration of harmful gases in order to ensure the staff’s health.

An Algorithm and Data Process Scheme for Indoor Location Based on Mobile Devices

Limited by the sampling capacity of the mobile devices, many real-time indoor location systems have such problems as low accuracy, large variance, and non-smooth movement of the estimated position. A new positioning algorithm and a new processing method for sampled data are proposed. Firstly, a positioning algorithm is designed based on the cluster-based nearest neighbour or probability. Secondly, a weighted average method with sliding window is used to process the sampled data as to overcome the mobile devices’ weak capability of signal sampling. Experimental results show that, for the general mobile devices, the accuracy of indoor position estimation increases from 56.5% to 76.6% for a 2-meter precision, and from 77.4% to 90.9% for a 3-meter precision. Therefore, the proposed methods can significantly and stably improve the positioning accuracy.

A NOVEL INDOOR GEO-LOCATION METHOD USING MIMO ARRAY

In a Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) based Wireless Local Area Network (WLAN) system, both Access Points (APs) and Mobile Termi-nals (MTs) are configured with multiple antennas, to make novel indoor geo-location method possible. In this paper, we presented a novel Least Square Support Vector Machine (LS-SVM) based data fusion algorithm to fuse signal strength measurements for indoor geo-location using only a single AP with MIMO arrays. We evaluate our proposed algorithms under indoor environments by MATLAB simulations. Simulation results show that our MIMO-based algorithm is superior to conventional least square algorithm.

The Improvement of Earthquake Real-Time Monitoring System of Chinese National Digital Seismic Network

The earthquake real-time monitoring system of the Chinese National Digital Seismic Network has been in operation since"the Ninth Five-year Plan"period,and the stability of the system has been well tested.In recent years,with the continuous improvement of monitoring technology and increase of public demands,the original real-time monitoring system needs to be upgraded and improved in terms of timeliness,stability,accuracy and ease of operation.Therefore,by accessing a total of more than 1,000 seismic stations,reducing the seismic trigger threshold of the monitoring system,eliminating the false trigger stations and optimizing the seismic waveform display interface,the current earthquake monitoring demands can be satisfied on the basis of ensuring the stable operation of the system.

Research Status and Trends of Indoor Positioning and Navigation Technology in China

As an essential component of future comprehensive Positioning,Navigation,and Timing(PNT)system,indoor positioning technology has extensive application demands,making it a focal point of attention in both academia and industry.This article comprehensively reviews the research status of indoor positioning technology in China,with a focus on highlighting representative achievements and application validations from major research institutions in recent years.It addresses the challenges and issues faced in promotion and application of large-scale,high-precision indoor positioning.Furthermore,a universal and seamless indoor-outdoor positioning system architecture is proposed,along with a technical roadmap and key technologies to achieve this architecture.Finally,an analysis and outlook on future technological trends are presented.

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

基于策略图的三维位置隐私发布算法研究

随着移动智能终端的普及,基于位置服务(Location-Based Services,LBS)的应用迎来了爆发式增长,高层室内建筑是位置服务的重要应用场景之一。然而现有的位置隐私保护算法大多适用于二维位置数据,面向大型室内三维场景的位置隐私保护研究尚且不足,并且缺乏可个性化定制的三维隐私策略。针对该问题,文章提出了一种基于策略图的三维位置隐私发布算法。首先,设计一种基于可定制策略图的位置隐私保护框架,可根据具体场景需求动态定制适合的隐私策略;其次,设计两种面向三维的差分隐私变体机制,结合定制策略图,实现三维场景下的位置隐私保护;最后,在三维数据集上进行仿真实验,实验结果表明,与其他三维位置隐私保护算法相比,文章所提算法具有更好的稳定性和效用性。

基于迁移学习的动态环境室内定位方法研究

随着智能家居应用的不断深化,基于Wi-Fi信号的室内定位技术也受到了广泛关注。在实际应用中,大多数室内定位算法采集得到的训练数据和测试数据通常并非来自于同一理想环境,各种环境条件变化以及信号漂移导致采集得到的训练数据和测试数据间的概率分布不同。传统定位模型在面对不同分布的训练数据和测试数据时无法保证具有良好的定位精度,常出现算法定位精度大幅降低,甚至算法不可用等问题。面对这一难点,迁移学习中的域适应方法作为一种可以有效解决训练样本和测试样本概率分布不一致的学习问题被广泛应用于室内定位领域。文中结合域适应学习和机器学习算法,提出了一种基于特征迁移的室内定位算法(Transfer Learning Location AlgorithmBased on Global and Local Metrics Adaptation,TL-GLMA)。TL-GLMA在定位阶段通过特征迁移方式将两域原始数据映射至高维空间,从而在最小化两域数据的分布差异的同时保留两域数据内部的局部几何属性,并利用映射后的独立同分布数据训练分类器,从而实现目标定位。实验结果表明,TL-GLMA能够有效减少环境变化带来的干扰,提升定位精度。

面向地图匹配的室内位置-语义模型设计

传统的室内空间信息模型或者过于复杂导致操作性较差,或者没有充分表达地图匹配所需的空间要素信息,阻碍地图匹配方法的高效实施。该研究将以构建能够有效支撑地图匹配的室内空间信息模型作为研究目标,此模型应明确室内空间要素的表达内容,并应确定具体的表达方法。模型的构建应以地图匹配方法的机理为基础,以室内地图匹配的实际需要为导向,以表达支撑室内地图匹配的空间信息为目的,分别对室内空间要素的位置及属性,室内空间要素之间的拓扑关系进行表达。该研究构建面向地图匹配的室内位置-语义模型。实验表明此模型较为简洁且信息完备,可在保证地图匹配实际需要的同时提高运行效率。

基于自适应距离ADWKNN室内定位算法

针对基于蓝牙接收信号强度(RSS)的加权K-近邻(WKNN)位置指纹室内定位算法中由于信号波动存在的单一距离估算标准导致定位精度偏低的问题,提出了一种基于自适应距离的ADWKNN定位算法。离线阶段利用K-means聚类算法对指纹数据库进行划分,减少数据查询量以保证定位的时效性。在线定位阶段首先对待定位点处采集到的RSS信号值进行卡尔曼滤波,减弱随机噪声的干扰;然后采用ADWKNN算法计算曼哈顿距离与欧氏距离的标准差,自适应的选择距离估算方法并实现K值的动态变化。实验结果表明,ADWKNN算法的平均定位精度为1.22 m,与使用余弦距离、曼哈顿距离、欧氏距离和Sorensen距离的单一距离的WKNN算法相比,ADWKNN算法的平均定位精度有明显提高。

5G信道状态信息信号质量及指纹定位性能分析

5G信道状态信息(channel state information,CSI)具有丰富的特征信息,是一种理想的指纹定位信号,但信号质量易受环境干扰,对定位性能影响较大.为了分析不同因素对5G信号质量和定位性能的影响程度,本文首先阐述了5G信号特征和基于支持向量回归(support vector regression,SVR)的定位算法,分析了数据采集时终端的高度、方向、人体遮挡等因素对信号质量的影响,测试了廊厅、小办公室和中型会议室三种场景下的定位性能.结果表明:5G信号质量受周围环境影响较大,在干扰较小的情况下,基于5G CSI的位置指纹定位算法在三种场景下的定位精度分别为0.93 m、1.46 m和1.94 m,能够满足大多数室内定位应用需求.

基于CSI相位采样密度的室内定位技术研究

基于信道状态信息(CSI)的室内定位技术已经被广泛应用于各种场合,在CSI相位指纹定位方案中,采样点密度大小与定位效果好坏紧密相关。由于数据采集工作量的关系,以往学者们多关注低采样密度场景或通过模拟仿真分析采样密度问题,导致难以找到定位误差最小的采样密度。本文设置不同的采样密度,将数据预处理后的CSI相位信息作为指纹,匹配WKNN算法,分析探究了不同采样点密度对于定位精度的影响。实验结果表明,在4 m×4 m的环境中,采样间隔设置为0.4 m,采样密度为7.6个/m2时,定位精度最高,平均误差为0.34 m,同时兼顾采样工作量,具有较高性价比。

一种融合蓝牙和地磁的位置指纹室内定位方法

室内定位服务目前已经普遍运用到了导航、抢险救灾等领域,其中基于蓝牙传感器的室内定位易于普及、实用性强,并且可以借助手机自带的一些功能实现,然而在人员较多和电磁环境复杂的区域往往定位效果较差。针对这个问题,本文提出了一种蓝牙与地磁融合的位置指纹室内定位方法,即在待定位的商场等区域设置若干蓝牙信标,采用手机自带的i Beacon功能和霍尔传感器分别接收蓝牙和地磁信号,之后对蓝牙估计坐标和地磁估计坐标在决策层进行融合计算用户的位置,既减小了定位误差又没有耗费额外的硬件资源。实验结果表明该融合定位方法具有较高的估计精度。

混合室内定位系统设计与实现

室内环境复杂性以及建筑物对信号的干扰使得GPS等室外定位无法精准作用于室内环境,因此提出一种混合室内定位系统,提高定位精准度,可较为直观地反映出定位地点的位置信息,一定程度上减少了定位所需时间。本文通过在一处居所的定位实验简单研究了混合定位的实现方式以及影响定位精度的因素,定位精度受数据库记录点密度、信号衰减系数、室内环境的变动等因素影响。