GSIS专辑精选| 无处不在的定位、室内导航和基于位置的服务（UPENLBS）

室内定位是未来人工智能的核心技术之一，对即将到来的人工智能时代起着举足轻重的作用。开发有效的室内定位新技术是工业界和学术界的研究热点。

然而，受室内复杂环境以及空间布局、拓扑易变等影响，实现准确、可靠、实时的室内定位，满足各类定位需求仍有很大的挑战性。

为此，《地球空间信息科学学报》（Geo-Spatial Information Science，GSIS）推出了“无处不在的定位、室内导航和基于位置的服务”（Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)）专辑，武汉大学测绘遥感信息工程国家重点实验室陈亮教授、陈锐志教授以及维也纳技术大学Guenther Retscher教授、武汉大学测绘遥感信息工程国家重点实验室柳景斌教授、武汉大学测绘学院潘元进副教授为专辑特邀客座编辑。

本期特刊由6篇室内定位相关的论文组成，对于目前使用智能手机、Wi-Fi信号中的指纹匹配技术、手机气压表等进行室内定位、无设备人体微活动识别、楼层定位的进展进行了较为全面的报道。扫描下方二维码或点击页尾“阅读全文”可免费阅读、下载本期特刊全部文章。

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01   

Indoor localization for pedestrians with real-time capability using multi-sensor smartphones

基于多传感器智能手机的室内行人实时定位

Catia Real Ehrlich & Jörg Blankenbach 

文章简介

人或物体的定位通常是指在空间参照系中确定的位置。室外通常是通过全球导航卫星系统（GNSS）来实现的，在没有GNSS的环境中，尤其是建筑物内部（室内）的人员自动定位是一个巨大的挑战。

在室内，卫星信号被建筑构件（如墙壁或天花板）衰减、屏蔽或反射。对于选定的应用，基于不同的技术（如WiFi、RFID或UWB），可以实现室内自动定位。然而，标准的解决方案仍然未有定论。许多室内定位系统仅适用于特定应用，或在特定条件下部署，例如附加基础设施或传感器技术。

智能手机作为一种流行的高性价比多传感器系统，是面向大众市场的室内定位平台，越来越受到人们的关注。今天的设备配备了多种传感器，可用于室内定位。本文提出了一种基于智能手机的行人室内定位方法。这种方法的新颖之处在于基于多传感器智能手机和易于安装的本地定位系统，对建筑物内的行人进行整体实时定位。为此，估计气压高度，以便得出用户所在的楼层。然后，根据从智能手机传感器中提取的用户运动，使用行人推算原理确定二维位置。为了使定位过程中由于各种传感器误差引起的强误差积累最小化，在位置估计中加入了附加信息。建筑模型用于为行人确定允许的（例如房间、通道）和不允许的（例如墙）建筑区域。此外，还包括一些有助于提高精度和鲁棒性的技术。针对不同线性和非线性数据的融合问题，提出了一种基于序贯蒙特卡罗方法的改进算法。

The localization of persons or objects usually refers to a position determined in a spatial reference system. Outdoors, this is usually accomplished with Global Navigation Satellite Systems (GNSS).

However, the automatic positioning of people in GNSS-free environments, especially inside of buildings (indoors) poses a huge challenge. Indoors, satellite signals are attenuated, shielded or reflected by building components (e.g. walls or ceilings). 

For selected applications, the automatic indoor positioning is possible based on different technologies (e.g. WiFi, RFID, or UWB). However, a standard solution is still not available. Many indoor positioning systems are only suitable for specific applications or are deployed under certain conditions, e.g. additional infrastructures or sensor technologies. Smartphones, as popular cost-effective multi-sensor systems, is a promising indoor localization platform for the mass-market and is increasingly coming into focus. Today’s devices are equipped with a variety of sensors that can be used for indoor positioning. In this contribution, an approach to smartphone-based pedestrian indoor localization is presented. 

The novelty of this approach refers to a holistic, real-time pedestrian localization inside of buildings based on multisensor smartphones and easy-to-install local positioning systems. 

For this purpose, the barometric altitude is estimated in order to derive the floor on which the user is located. The 2D position is determined subsequently using the principle of pedestrian dead reckoning based on user's movements extracted from the smartphone sensors. In order to minimize the strong error accumulation in the localization caused by various sensor errors, additional information is integrated into the position estimation. The building model is used to identify permissible (e.g. rooms, passageways) and impermissible (e.g. walls) building areas for the pedestrian. Several technologies contributing to higher precision and robustness are also included. For the fusion of different linear and non-linear data, an advanced algorithm based on the Sequential Monte Carlo method is presented.

02  

A map-matching algorithm dealing with sparse cellular fingerprint observations

一种处理稀疏细胞指纹观测的地图匹配算法

Andrea Dalla Torre, Paolo Gallo, Donatella Gubiani, Chris Marshall, Angelo Montanari, Federico Pittino & Andrea Viel

文章简介

移动通信的广泛可用性使得移动设备成为收集有关移动基础设施和用户移动性的数据的资源。在这种情况下，根据观测到的位置序列重建道路网络上设备最可能的轨迹的问题（地图匹配问题）变得尤为重要。不同的贡献表明，即使只有一组稀疏的全球导航卫星系统位置可用，以高精度重建设备轨迹在技术上是可行的。

在这篇论文中，我们面对的问题是如何处理稀疏的细胞指纹序列。与全球导航卫星系统的位置相比，细胞指纹提供了更粗糙的空间信息，但即使设备丢失了全球导航卫星系统的位置或以节能模式运行，它们也能工作。我们设计了一种新的地图匹配算法，利用著名的隐马尔可夫模型和随机森林，成功地处理了噪声和稀疏的细胞观测。通过改变观测数据的采样和指纹图的密度，以及在指纹观测序列中加入一些GPS位置，在意大利一个中等城市环境中测试了所提解决方案的性能。

The widespread availability of mobile communication makes mobile devices a resource for the collection of data about mobile infrastructures and user mobility. 

In these contexts, the problem of reconstructing the most likely trajectory of a device on the road network on the basis of the sequence of observed locations (map-matching problem) turns out to be particularly relevant. Different contributions have demonstrated that the reconstruction of the trajectory of a device with good accuracy is technically feasible even when only a sparse set of GNSS positions is available. 

In this paper, we face the problem of coping with sparse sequences of cellular fingerprints. Compared to GNSS positions, cellular fingerprints provide coarser spatial information, but they work even when a device is missing GNSS positions or is operating in an energy saving mode. 

We devise a new map-matching algorithm, that exploits the well-known Hidden Markov Model and Random Forests to successfully deal with noisy and sparse cellular observations. 

The performance of the proposed solution has been tested over a medium-sized Italian city urban environment by varying both the sampling of the observations and the density of the fingerprint map as well as by including some GPS positions into the sequence of fingerprint observations.

03 

A regression model-based method for indoor positioning with compound location fingerprints

一种基于回归模型的复合定位指纹室内定位方法

Tomofumi Takayama, Takeshi Umezawa, Nobuyoshi Komuro & Noritaka Osawa

文章简介

本文提出并评价了一种室内定位的估计方法。该方法结合了位置指纹和航位推算，不同于传统的组合。它使用复合位置指纹，由多个时间点（即多个位置）的无线电指纹和通过航位推算估计它们之间的位移组成的复合位置指纹。

为了避免航位推算积累的误差，该方法采用短程航位推算。该方法使用安装在一间11×5米的学生房内的16个蓝牙信标进行了评估。在30个测量点收集信标的接收信号强度指标（RSSI）值，这些测量点位于1×1m网格上没有障碍物的交叉点。复合定位指纹由两点的RSSI矢量和它们之间的位移矢量组成。随机森林（RF）被用来建立回归模型来估计位置指纹。使用16个蓝牙信标，位置估计的均方根误差为0.87米。这种误差比单点基线模型的误差要小，在单点基线模型中，特征向量只由一个位置的RSSI值组成。结果表明，该方法对室内定位是有效的。

This paper proposed and evaluated an estimation method for indoor positioning. The method combines location fingerprinting and dead reckoning differently from the conventional combinations.

It uses compound location fingerprints, which are composed of radio fingerprints at multiple points of time, that is, at multiple positions, and displacements between them estimated by dead reckoning. To avoid errors accumulated from dead reckoning, the method uses short-range dead reckoning. 

The method was evaluated using 16 Bluetooth beacons installed in a student room with the dimensions of 11 × 5 m with furniture inside. The Received Signal Strength Indicator (RSSI) values of the beacons were collected at 30 measuring points, which were points at the intersections on a 1 × 1 m grid with no obstacles. 

A compound location fingerprint is composed of RSSI vectors at two points and a displacement vector between them. Random Forests (RF) was used to build regression models to estimate positions from location fingerprints. The root mean square error of position estimation was 0.87 m using 16 Bluetooth beacons.

This error is lower than that received with a single-point baseline model, where a feature vector is composed of only RSSI values at one location. The results suggest that the proposed method is effective for indoor positioning. 

04   

Low-complexity online correction and calibration of pedestrian dead reckoning using map matching and GPS

基于地图匹配和GPS的行人航位推算的低复杂度在线校正与标定

Fabian Hölzke, Johann-P. Wolff, Frank Golatowski & Christian Haubelt

文章简介

航位推算是一种相对定位方案，用于通过测量行驶距离和方向变化来推断相对于原点的位置变化。行人航位推算（PDR）将此概念应用于步行的人。这种方法可以用来跟踪某人在一座建筑物中的移动，在一个已知的地标，如建筑物的入口被注册之后。这里，对一只脚的运动和相应的方向变化进行测量和总结，从而推断出当前的位置。测量和整合相应的物理参数，例如使用惯性传感器，会引入小误差，这些小误差会迅速累积为大的距离误差。了解建筑物的地理位置可以减少这些误差，因为它可以防止估计的位置从墙壁移动到可能的路径上。

在本文中，我们使用建筑地图来改善基于单脚安装惯性传感器的定位。使用零速度更新来精确计算单个步骤的长度，并使用Madgwick滤波器来确定步骤的方向。尽管单个步骤的计算相当精确，但长期来看，小误差仍会累积。我们展示了使用可能路径和不可能路径的校正算法如何校正行人航位推算任务的固有误差，如方位和位移漂移，并讨论了这些算法的限制和缺点。

我们还提出了一种从GPS测量中获得初始位置和方位的方法。验证了本文提出的PDR校正方法，分析了6名参与者在办公楼中行走四条不同长度和复杂度的路线，每条路线走了三次。定量结果表明，当使用可能的路径时，端点精度提高了60%，而在使用不太可能的路径时，提高了23%。然而，这两种方法在某些情况下也会降低准确性。我们确定了这些场景，并为改进行人航位推算方法提供了进一步的思路。

Dead Reckoning is a relative positioning scheme that is used to infer the change of position relative to a point of origin by measuring the traveled distance and orientation change. Pedestrian Dead Reckoning (PDR) applies this concept to walking persons. 

The method can be used to track someone's movement in a building after a known landmark like the building's entrance is registered. 

Here, the movement of a foot and the corresponding direction change is measured and summed up, to infer the current position. Measuring and integrating the corresponding physical parameters, e.g. using inertial sensors, introduces small errors that accumulate quickly into large distance errors. Knowledge of a buildings geography may reduce these errors as it can be used to keep the estimated position from moving through walls and onto likely paths. 

In this paper, we use building maps to improve localization based on a single foot-mounted inertial sensor. We describe our localization method using zero velocity updates to accurately compute the length of individual steps and a Madgwick filter to determine the step orientation. 

Even though the computation of individual steps is quite accurate, small errors still accumulate in the long term. We show how correction algorithms using likely and unlikely paths can rectify errors intrinsic to pedestrian dead reckoning tasks, such as orientation and displacement drift, and discuss restrictions and disadvantages of these algorithms. 

We also present a method of deriving the initial position and orientation from GPS measurements. We verify our PDR correction methods analyzing the corrected and raw trajectories of six participants walking four routes of varying length and complexity through an office building, walking each route three times. 

Our quantitative results show an endpoint accuracy improvement of up to 60% when using likely paths and 23% when using unlikely paths. However, both approaches can also decrease accuracy in certain scenarios. We identify those scenarios and offer further ideas for improving Pedestrian Dead Reckoning methods.

05   

Device-free human micro-activity recognition method using WiFi signals

基于WiFi信号的无设备人体微活动识别方法

Mohammed A. A. Al-qaness

文章简介

人类活动跟踪在人机交互中起着至关重要的作用。传统的人类活动识别（HAR）方法采用摄像机和传感器等特殊设备来跟踪宏观和微观活动。近年来，无线信号被用来在室内环境中跟踪人类的运动和活动，而不需要额外的设备。本研究提出了一种利用无线信号的信道状态信息（CSI）的无设备WiFi微活动识别方法。

与现有的基于CSI的微活动识别方法不同，该方法从CSI中提取幅度和相位信息，从而提供更多的信息，提高检测精度。该方法利用一种有效的信号处理技术来揭示每个活动的独特模式。我们采用机器学习算法来识别所提出的微活动。所提出的方法已经在视线（LOS）和非视线（NLOS）情况下进行了评估，实验结果验证了该方法的有效性。

Human activity tracking plays a vital role in human–computer interaction. Traditional human activity recognition (HAR) methods adopt special devices, such as cameras and sensors, to track both macro- and micro-activities. Recently, wireless signals have been exploited to track human motion and activities in indoor environments without additional equipment. 

This study proposes a device-free WiFi-based micro-activity recognition method that leverages the channel state information (CSI) of wireless signals. Different from existed CSI-based microactivity recognition methods, the proposed method extracts both amplitude and phase information from CSI, thereby providing more information and increasing detection accuracy. 

The proposed method harnesses an effective signal processing technique to reveal the unique patterns of each activity. We applied a machine learning algorithm to recognize the proposed micro-activities. The proposed method has been evaluated in both line of sight (LOS) and none line of sight (NLOS) scenarios, and the empirical results demonstrate the effectiveness of the proposed method with several users.

06  

Floor positioning method indoors with smartphone’s barometer

智能手机气压表室内楼层定位方法

Min Yu, Feng Xue, Chao Ruan & Hang Guo

文章简介

针对传统楼层定位技术的低可用性和高环境依赖性问题，提出了一种基于智能手机气压表的室内楼层定位方法。

首先，得到了一种带有“进入”检测算法的初始楼层位置算法。其次，根据气压波动的特征来识别用户的上下楼活动。第三，通过估计垂直方向的移动距离和上下楼时的楼层变化，得到准确的楼层位置。

为了解决不同手机气压表对楼层的误判问题，本文对不同手机的气压数据进行了计算，有效地减少了由于手机的不均匀性造成的气压估计高程误差。实验结果表明，三种手机楼层识别的平均正确率均在85%以上，同时降低了环境依赖性，提高了可用性。

此外，本文还对三种常用的楼层定位方法：基于指纹的WLAN楼层定位（WFL）、神经网络楼层定位（NFL）和磁性楼层定位（MFL）与我们的方法进行了比较和分析。实验结果表明，利用华为mate10pro手机进行楼层识别的正确率达到94.2%。

This paper presents an indoor floor positioning method with the smartphone’s barometer for the purpose of solving the problem of low availability and high environmental dependence of the traditional floor positioning technology. 

First, an initial floor position algorithm with the “entering” detection algorithm has been obtained. Second, the user’s going upstairs or downstairs activities are identified by the characteristics of the air pressure fluctuation. Third, the moving distance in the vertical direction and the floor change during going upstairs or downstairs are estimated to obtain the accurate floor position. 

In order to solve the problem of the floor misjudgment from different mobile phone’s barometers, this paper calculates the pressure data from the different cell phones, and effectively reduce the errors of the air pressure estimating the elevation which is caused by the heterogeneity of the mobile phones. 

The experiment results show that the average correct rate of the floor identification is more than 85% for three types of the cell phones while reducing environmental dependence and improving availability. Further, this paper compares and analyzes the three common floor location methods – the WLAN Floor Location (WFL) method based on the fingerprint, the Neural Network Floor Location (NFL) methods, and the Magnetic Floor Location (MFL) method with our method.

The experiment results achieve 94.2% correct rate of the floor identification with Huawei mate10 Pro mobile phone.than 85% for three types of the cell phones while reducing environmental dependence and improving availability. 

Further, this paper compares and analyzes the three common floor location methods – the WLAN Floor Location (WFL) method based on the fingerprint, the Neural Network Floor Location (NFL) methods, and the Magnetic Floor Location (MFL) method with our method. The experiment results achieve 94.2% correct rate of the floor identification with Huawei mate10 Pro mobile phone.

关于  Geo-spatial Information Science

Geo-spatial Information Science（GSIS）是由武汉大学主办的测绘遥感专业英文期刊，主编为中国科学院院士、中国工程院院士李德仁教授。2020年9月被SCIE收录。

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