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地球空間信息科學學報GSIS

武漢大學《地球空間信息科學學報》 Geo-spatial Information Science （GSIS）官方帳號

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室內定位是未來人工智慧的核心技術之一，對即將到來的人工智慧時代起著舉足輕重的作用。開發有效的室內定位新技術是工業界和學術界的研究熱點。

然而，受室內複雜環境以及空間布局、拓撲易變等影響，實現準確、可靠、實時的室內定位，滿足各類定位需求仍有很大的挑戰性。

為此，《地球空間信息科學學報》（Geo-Spatial Information Science，GSIS）推出了「無處不在的定位、室內導航和基於位置的服務」（Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)）專輯，武漢大學測繪遙感信息工程國家重點實驗室陳亮教授、陳銳志教授以及維也納技術大學Guenther Retscher教授、武漢大學測繪遙感信息工程國家重點實驗室柳景斌教授、武漢大學測繪學院潘元進副教授為專輯特邀客座編輯。

本期特刊由6篇室內定位相關的論文組成，對於目前使用智慧型手機、Wi-Fi信號中的指紋匹配技術、手機氣壓表等進行室內定位、無設備人體微活動識別、樓層定位的進展進行了較為全面的報導。掃描下方二維碼或點擊頁尾「閱讀全文」可免費閱讀、下載本期特刊全部文章。

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1

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

基於多傳感器智慧型手機的室內行人實時定位

Catia Real Ehrlich & Jörg Blankenbach

文章簡介

人或物體的定位通常是指在空間參照系中確定的位置。室外通常是通過全球導航衛星系統（GNSS）來實現的，在沒有GNSS的環境中，尤其是建築物內部（室內）的人員自動定位是一個巨大的挑戰。

在室內，衛星信號被建築構件（如牆壁或天花板）衰減、屏蔽或反射。對於選定的應用，基於不同的技術（如WiFi、RFID或UWB），可以實現室內自動定位。然而，標準的解決方案仍然未有定論。許多室內定位系統僅適用於特定應用，或在特定條件下部署，例如附加基礎設施或傳感器技術。

智慧型手機作為一種流行的高性價比多傳感器系統，是面向大眾市場的室內定位平臺，越來越受到人們的關注。今天的設備配備了多種傳感器，可用於室內定位。本文提出了一種基於智慧型手機的行人室內定位方法。這種方法的新穎之處在於基於多傳感器智慧型手機和易於安裝的本地定位系統，對建築物內的行人進行整體實時定位。為此，估計氣壓高度，以便得出用戶所在的樓層。然後，根據從智慧型手機傳感器中提取的用戶運動，使用行人推算原理確定二維位置。為了使定位過程中由於各種傳感器誤差引起的強誤差積累最小化，在位置估計中加入了附加信息。建築模型用於為行人確定允許的（例如房間、通道）和不允許的（例如牆）建築區域。此外，還包括一些有助於提高精度和魯棒性的技術。針對不同線性和非線性數據的融合問題，提出了一種基於序貫蒙特卡羅方法的改進算法。

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.

2

A map-matching algorithm dealing with sparse cellular fingerprint observations

一種處理稀疏細胞指紋觀測的地圖匹配算法

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

文章簡介

移動通信的廣泛可用性使得行動裝置成為收集有關移動基礎設施和用戶移動性的數據的資源。在這種情況下，根據觀測到的位置序列重建道路網絡上設備最可能的軌跡的問題（地圖匹配問題）變得尤為重要。不同的貢獻表明，即使只有一組稀疏的全球導航衛星系統位置可用，以高精度重建設備軌跡在技術上是可行的。

在這篇論文中，我們面對的問題是如何處理稀疏的細胞指紋序列。與全球導航衛星系統的位置相比，細胞指紋提供了更粗糙的空間信息，但即使設備丟失了全球導航衛星系統的位置或以節能模式運行，它們也能工作。我們設計了一種新的地圖匹配算法，利用著名的隱馬爾可夫模型和隨機森林，成功地處理了噪聲和稀疏的細胞觀測。通過改變觀測數據的採樣和指紋圖的密度，以及在指紋觀測序列中加入一些GPS位置，在義大利一個中等城市環境中測試了所提解決方案的性能。

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.

3

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

一種基於回歸模型的複合定位指紋室內定位方法

Tomofumi Takayama, Takeshi Umezawa, Nobuyoshi Komuro & Noritaka Osawa

文章簡介

本文提出並評價了一種室內定位的估計方法。該方法結合了位置指紋和航位推算，不同於傳統的組合。它使用複合位置指紋，由多個時間點（即多個位置）的無線電指紋和通過航位推算估計它們之間的位移組成的複合位置指紋。

為了避免航位推算積累的誤差，該方法採用短程航位推算。該方法使用安裝在一間11×5米的學生房內的16個藍牙信標進行了評估。在30個測量點收集信標的接收信號強度指標（RSSI）值，這些測量點位於1×1m網格上沒有障礙物的交叉點。複合定位指紋由兩點的RSSI矢量和它們之間的位移矢量組成。隨機森林（RF）被用來建立回歸模型來估計位置指紋。使用16個藍牙信標，位置估計的均方根誤差為0.87米。這種誤差比單點基線模型的誤差要小，在單點基線模型中，特徵向量只由一個位置的RSSI值組成。結果表明，該方法對室內定位是有效的。

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.

4

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

基於地圖匹配和GPS的行人航位推算的低複雜度在線校正與標定

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

文章簡介

航位推算是一種相對定位方案，用於通過測量行駛距離和方向變化來推斷相對於原點的位置變化。行人航位推算（PDR）將此概念應用於步行的人。這種方法可以用來跟蹤某人在一座建築物中的移動，在一個已知的地標，如建築物的入口被註冊之後。這裡，對一隻腳的運動和相應的方向變化進行測量和總結，從而推斷出當前的位置。測量和整合相應的物理參數，例如使用慣性傳感器，會引入小誤差，這些小誤差會迅速累積為大的距離誤差。了解建築物的地理位置可以減少這些誤差，因為它可以防止估計的位置從牆壁移動到可能的路徑上。

在本文中，我們使用建築地圖來改善基於單腳安裝慣性傳感器的定位。使用零速度更新來精確計算單個步驟的長度，並使用Madgwick濾波器來確定步驟的方向。儘管單個步驟的計算相當精確，但長期來看，小誤差仍會累積。我們展示了使用可能路徑和不可能路徑的校正算法如何校正行人航位推算任務的固有誤差，如方位和位移漂移，並討論了這些算法的限制和缺點。

我們還提出了一種從GPS測量中獲得初始位置和方位的方法。驗證了本文提出的PDR校正方法，分析了6名參與者在辦公樓中行走四條不同長度和複雜度的路線，每條路線走了三次。定量結果表明，當使用可能的路徑時，端點精度提高了60%，而在使用不太可能的路徑時，提高了23%。然而，這兩種方法在某些情況下也會降低準確性。我們確定了這些場景，並為改進行人航位推算方法提供了進一步的思路。

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.

5

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

基於WiFi信號的無設備人體微活動識別方法

Mohammed A. A. Al-qaness

文章簡介

人類活動跟蹤在人機互動中起著至關重要的作用。傳統的人類活動識別（HAR）方法採用攝像機和傳感器等特殊設備來跟蹤宏觀和微觀活動。近年來，無線信號被用來在室內環境中跟蹤人類的運動和活動，而不需要額外的設備。本研究提出了一種利用無線信號的信道狀態信息（CSI）的無設備WiFi微活動識別方法。

與現有的基於CSI的微活動識別方法不同，該方法從CSI中提取幅度和相位信息，從而提供更多的信息，提高檢測精度。該方法利用一種有效的信號處理技術來揭示每個活動的獨特模式。我們採用機器學習算法來識別所提出的微活動。所提出的方法已經在視線（LOS）和非視線（NLOS）情況下進行了評估，實驗結果驗證了該方法的有效性。

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.

6

Floor positioning method indoors with smartphone’s barometer

智慧型手機氣壓表室內樓層定位方法

Min Yu, Feng Xue, Chao Ruan & Hang Guo

文章簡介

針對傳統樓層定位技術的低可用性和高環境依賴性問題，提出了一種基於智慧型手機氣壓表的室內樓層定位方法。

首先，得到了一種帶有「進入」檢測算法的初始樓層位置算法。其次，根據氣壓波動的特徵來識別用戶的上下樓活動。第三，通過估計垂直方向的移動距離和上下樓時的樓層變化，得到準確的樓層位置。

為了解決不同手機氣壓表對樓層的誤判問題，本文對不同手機的氣壓數據進行了計算，有效地減少了由於手機的不均勻性造成的氣壓估計高程誤差。實驗結果表明，三種手機樓層識別的平均正確率均在85%以上，同時降低了環境依賴性，提高了可用性。

此外，本文還對三種常用的樓層定位方法：基於指紋的WLAN樓層定位（WFL）、神經網絡樓層定位（NFL）和磁性樓層定位（MFL）與我們的方法進行了比較和分析。實驗結果表明，利用華為mate10pro手機進行樓層識別的正確率達到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.

關於 Geo-spatial Information Science

Geo-spatial Information Science（GSIS）是由武漢大學主辦的測繪遙感專業英文期刊，主編為中國科學院院士、中國工程院院士李德仁教授。2020年9月被SCIE收錄。

GSIS 採用開放獲取的出版模式，就是大家所說的開源期刊/OA期刊（Open Access），文章一經發表，可馬上被全球讀者免費全文下載，這種模式可以讓你的文章有更多的曝光度。

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期刊官網：https://www.tandfonline.com/tgsi

投稿網址：https://rp.tandfonline.com/submission/create?journalCode=TGSI；

來源：地球空間信息科學學報GSIS

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原標題：《GSIS專輯精選 | 無處不在的定位、室內導航和基於位置的服務（UPENLBS）》

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