Machine Learning-based Real-Time Indoor Landmark Localization

Nowadays, smartphones can collect huge amounts of data from their surroundings with the help of highly accurate sensors. Since the combination of the Received Signal Strengths of surrounding access points and sensor data is assumed to be unique in some locations, it is possible to use this information to accurately predict smartphones' indoor locations. In this work, we apply machine learning methods to derive the correlation between smartphones' locations and the received Wi-Fi signal strength and sensor values. We have developed an Android application that is able to distinguish between rooms on a floor, and special landmarks within the detected room. Our real-world experiment results show that the Voting ensemble predictor outperforms individual machine learning algorithms and it achieves the best indoor landmark localization accuracy of 94% in office-like environments. This work provides a coarse-grained indoor room recognition and landmark localization within rooms, which can be envisioned as a basis for accurate indoor positioning

Automatic Wi-Fi Fingerprint System based on Unsupervised Learning

Recently, smartphones and Wi-Fi appliances have been generalized in daily life, and location-based service(LBS) has gradually been extended to indoor environments. Unlike outdoor positioning, which is typically handled by the global positioning system(GPS), indoor positioning technologies for providing LBSs have been studied with algorithms using various short-range wireless communications such as Wi-Fi, Ultra-wideband, Bluetooth, etc. Fingerprint-based positioning technology, a representative indoor LBS, estimates user locations using the received signal strength indicator(RSSI), indicating the relative transmission power of the access point(AP). Therefore, a fingerprint-based algorithm has the advantage of being robust to distorted wireless environments, such as radio wave reflections and refractions, compared to the time-of-arrival(TOA) method for non-line-of-sight(NLOS), where many obstacles exist. Fingerprint is divided into a training phase in which a radio map is generated by measuring the RSSIs of all indoor APs and positioning phase in which the positions of users are estimated by comparing the RSSIs of the generated radio map in real-time. In the training phase, the user collects the RSSIs of all APs measured at reference points set at regular intervals of 2 to 3m, creating a radio map. In the positioning phase, the reference point, which is most similar to the RSSI, compares the generated radio map from the training phase to the RSSI measured from user movements. This estimates the real-time indoor position. Fingerprint algorithms based on supervised and semi-supervised learning such as support vector machines and principal component analysis are essential for measuring the RSSIs in all indoor areas to produce a radio map. As the building size and the complexity of structures increases, the amount of work and time required also increase. The radio map generation algorithm that uses channel modeling does not require direct measurement, but it requires considerable effort because of building material, three-dimensional reflection coefficient, and numerical modeling of all obstacles. To overcome these problems, this thesis proposes an automatic Wi-Fi fingerprint system that combines an unsupervised dual radio mapping(UDRM) algorithm that reduces the time taken to acquire Wi-Fi signals and leverages an indoor environment with a minimum description length principle(MDLP)-based radio map feedback(RMF) algorithm to simultaneously optimize and update the radio map. The proposed UDRM algorithm in the training phase generates a radio map of the entire building based on the measured radio map of one reference floor by selectively applying the autoencoder and the generative adversarial network(GAN) according to the spatial structures. The proposed learning-based UDRM algorithm does not require labeled data, which is essential for supervised and semi-supervised learning algorithms. It has a relatively low dependency on RSSI datasets. Additionally, it has a high accuracy of radio map prediction than existing models because it learns the indoor environment simultaneously via a indoor two-dimensional map(2-D map). The produced radio map is used to estimate the real-time positioning of users in the positioning phase. Simultaneously, the proposed MDLP-based RMF algorithm analyzes the distribution characteristics of the RSSIs of newly measured APs and feeds the analyzed results back to the radio map. The MDLP, which is applied to the proposed algorithm, improves the performance of the positioning and optimizes the size of the radio map by preventing the indefinite update of the RSSI and by updating the newly added APs to the radio map. The proposed algorithm is compared with a real measurement-based radio map, confirming the high stability and accuracy of the proposed fingerprint system. Additionally, by generating a radio map of indoor areas with different structures, the proposed system is shown to be robust against the change in indoor environment, thus reducing the time cost. Finally, via a euclidean distance-based experiment, it is confirmed that the accuracy of the proposed fingerprint system is almost the same as that of the RSSI-based fingerprint system.|최근 스마트폰과 Wi-Fi가 실생활에 보편화되면서 위치기반 서비스에 대한 개발 분야가 실내 환경으로 점차 확대되고 있다. GPS로 대표되는 실외 위치 인식과 달리 위치기반 서비스를 제공하기 위한 실내 위치 인식 기술은 Wi-Fi, UWB, 블루투스 등과 같은 다양한 근거리 무선 통신 기반의 알고리즘들이 연구되고 있다. 대표적인 실내 위치인식 알고리즘 중 하나인 Fingerprint는 사용자가 수신한 AP 신호의 상대적인 크기를 나타내는 RSSI를 사용하여 위치를 추정한다. 따라서 Fingerprint기반의 알고리즘은 장애물이 많이 존재하는 비가시 거리에서 TOA 방식에 비해 전파의 반사 및 굴절과 같이 왜곡된 무선 환경에 강인하다는 장점이 있다. Fingerprint는 실내의 모든 AP의 RSSI들을 측정하여 Radio map을 제작하는 과정인 학습 단계와 생성된 Radio map의 RSSI들을 실시간으로 측정된 RSSI와 비교하여 사용자의 위치를 추정하는 위치인식 단계로 나누어진다. 학습 단계에서는 위치를 구분하기 위하여 사용자가 2~3m의 일정한 간격으로 설정된 참조 위치들마다 측정되는 모든 AP들의 RSSI를 수집하고 Radio map으로 제작한다. 위치인식 단계에서는 학습 단계에서 제작된 Radio map과 사용자의 이동에 의해 측정되는 RSSI의 비교를 통해 가장 유사한 RSSI 패턴을 가지는 참조 위치가 실시간 실내 위치로 추정된다. 서포트 벡터 머신(SVM), 주성분 분석(PCA) 등과 같이 지도 및 준지도 학습기반의 Fingerprint 알고리즘은 Radio map을 제작하기 위해 모든 실내 공간에서 RSSI의 측정이 필수적이다. 이러한 알고리즘들은 건물이 대형화되고 구조가 복잡해질수록 측정 공간이 늘어나면서 작업과 시간 소모가 또한 급격히 증가한다. 채널모델링을 통한 Radio map 생성 알고리즘은 직접적인 측정 과정이 불필요한 반면에 건물의 재질, 3차원적인 구조에 따른 반사 계수 및 모든 장애물에 대한 수치적인 모델링이 필수적이기 때문에 상당히 많은 작업량이 요구된다. 따라서 본 논문에서는 이러한 문제점들을 해결하고자 학습 단계에서 Wi-Fi 신호의 수집시간을 최소화하면서 실내 환경이 고려된 Unsupervised Dual Radio Mapping(UDRM) 알고리즘과 위치인식 단계에서 Radio map의 최적화가 동시에 가능한 Minimum description length principle(MDLP)기반의 Radio map Feedback(RMF) 알고리즘이 결합된 비지도학습기반의 자동 Wi-Fi Fingerprint를 제안한다. 학습 단계에서 제안하는 UDRM 알고리즘은 뉴럴 네트워크 기반의 비지도 학습 알고리즘인 Autoencoder와 Generative Adversarial Network (GAN)를 공간구조에 따라 선택적으로 적용하여 하나의 참조 층에서 측정된 Radio map을 기반으로 건물전체의 Radio map을 생성한다. 제안하는 비지도 학습 기반 UDRM 알고리즘은 지도 및 준지도 학습에서 필수적인 Labeled data가 필요하지 않으며 RSSI 데이터 세트의 의존성이 상대적으로 낮다. 또한 2차원 실내 지도를 통해 실내 환경을 동시에 학습하기 때문에 기존의 예측 모델에 비해 Radio map의 예측 정확도가 높다. 제안한 알고리즘에 의해 제작된 Radio map은 위치인식 단계에서 사용자의 실시간 위치인식에 적용된다. 동시에 제안하는 MDLP 기반의 자동 Wi-Fi 업데이트 알고리즘은 새롭게 측정되는 AP들의 RSSI의 분포특성을 분석하고 그 결과를 Radio map에 피드백한다. 제안한 알고리즘에 적용된 MDLP는 무분별한 RSSI의 업데이팅을 방지하고 추가되는 AP를 Radio map에 업데이트함으로서 위치인식의 성능을 향상시키고 Radio map의 크기의 최적화가 가능하다. 제안한 알고리즘은 실제 측정기반의 Radio map과 서로 비교를 통해 제안한 Fingerprint 시스템의 높은 안정성과 정확도를 확인하였다. 또한 구조가 다른 실내공간의 Radio map 생성 결과를 통해 실내 환경 변화에 강인함과 학습 시간 측정을 통한 시간 비용이 감소함을 확인하였다. 마지막으로 Euclidean distance 기반 실험을 통하여 실제 측정한 RSSI기반의 Fingerprint 시스템과 제안한 시스템의 위치인식 정확도가 거의 일치함을 확인하였다.Contents Contents ⅰ Lists of Figures and Tables ⅲ Abstract ⅵ Chapter 1 Introduction 01 1.1 Background and Necessity for Research 01 1.2 Objectives and Contents for Research 04 Chapter 2 Wi-Fi Positioning and Unsupervised Learning 07 2.1 Wi-Fi Positioning 07 2.1.1 Wi-Fi Signal and Fingerprint 07 2.1.2 Fingerprint Techniques 15 2.2 Unsupervised Learning 23 2.2.1 Neural Network 23 2.2.2 Autoencoder 28 2.2.3 Generative Adversarial Network 31 Chapter 3 Proposed Fingerprint System 36 3.1 Unsupervised Dual Radio Mapping Algorithm 36 3.2 MDLP-based Radio Map Feedback Algorithm 47 Chapter 4 Experiment and Result 51 4.1 Experimental Environment and Configuration 51 4.2 Results of Unsupervised Dual Radio Mapping Algorithm 56 4.2 Results of MDLP-based Radio Map Feedback Algorithm 69 Chapter 5 Conclusion 79 Reference 81Docto

WLAN-paikannuksen elinkaaren tukeminen

The advent of GPS positioning at the turn of the millennium provided consumers with worldwide access to outdoor location information. For the purposes of indoor positioning, however, the GPS signal rarely penetrates buildings well enough to maintain the same level of positioning granularity as outdoors. Arriving around the same time, wireless local area networks (WLAN) have gained widespread support both in terms of infrastructure deployments and client proliferation. A promising approach to bridge the location context then has been positioning based on WLAN signals. In addition to being readily available in most environments needing support for location information, the adoption of a WLAN positioning system is financially low-cost compared to dedicated infrastructure approaches, partly due to operating on an unlicensed frequency band. Furthermore, the accuracy provided by this approach is enough for a wide range of location-based services, such as navigation and location-aware advertisements. In spite of this attractive proposition and extensive research in both academia and industry, WLAN positioning has yet to become the de facto choice for indoor positioning. This is despite over 20 000 publications and the foundation of several companies. The main reasons for this include: (i) the cost of deployment, and re-deployment, which is often significant, if not prohibitive, in terms of work hours; (ii) the complex propagation of the wireless signal, which -- through interaction with the environment -- renders it inherently stochastic; (iii) the use of an unlicensed frequency band, which means the wireless medium faces fierce competition by other technologies, and even unintentional radiators, that can impair traffic in unforeseen ways and impact positioning accuracy. This thesis addresses these issues by developing novel solutions for reducing the effort of deployment, including optimizing the indoor location topology for the use of WLAN positioning, as well as automatically detecting sources of cross-technology interference. These contributions pave the way for WLAN positioning to become as ubiquitous as the underlying technology.GPS-paikannus avattiin julkiseen käyttöön vuosituhannen vaihteessa, jonka jälkeen sitä on voinut käyttää sijainnin paikantamiseen ulkotiloissa kaikkialla maailmassa. Sisätiloissa GPS-signaali kuitenkin harvoin läpäisee rakennuksia kyllin hyvin voidakseen tarjota vastaavaa paikannustarkkuutta. Langattomat lähiverkot (WLAN), mukaan lukien tukiasemat ja käyttölaitteet, yleistyivät nopeasti samoihin aikoihin. Näiden verkkojen signaalien käyttö on siksi alusta asti tarjonnut lupaavia mahdollisuuksia sisätilapaikannukseen. Useimmissa ympäristöissä on jo valmiit WLAN-verkot, joten paikannuksen käyttöönotto on edullista verrattuna järjestelmiin, jotka vaativat erillisen laitteiston. Tämä johtuu osittain lisenssivapaasta taajuusalueesta, joka mahdollistaa kohtuuhintaiset päätelaitteet. WLAN-paikannuksen tarjoama tarkkuus on lisäksi riittävä monille sijaintipohjaisille palveluille, kuten suunnistamiselle ja paikkatietoisille mainoksille. Näistä lupaavista alkuasetelmista ja laajasta tutkimuksesta huolimatta WLAN-paikannus ei ole kuitenkaan pystynyt lunastamaan paikkaansa pääasiallisena sisätilapaikannusmenetelmänä. Vaivannäöstä ei ole puutetta; vuosien saatossa on julkaistu yli 20 000 tieteellistä artikkelia sekä perustettu useita yrityksiä. Syitä tähän kehitykseen on useita. Ensinnäkin, paikannuksen pystyttäminen ja ylläpito vaativat aikaa ja vaivaa. Toiseksi, langattoman signaalin eteneminen ja vuorovaikutus ympäristön kanssa on hyvin monimutkaista, mikä tekee mallintamisesta vaikeaa. Kolmanneksi, eri teknologiat ja laitteet kilpailevat lisenssivapaan taajuusalueen käytöstä, mikä johtaa satunnaisiin paikannustarkkuuteen vaikuttaviin tietoliikennehäiriöihin. Väitöskirja esittelee uusia menetelmiä joilla voidaan merkittävästi pienentää paikannusjärjestelmän asennuskustannuksia, jakaa ympäristö automaattisesti osiin WLAN-paikannusta varten, sekä tunnistaa mahdolliset langattomat häiriölähteet. Nämä kehitysaskeleet edesauttavat WLAN-paikannuksen yleistymistä jokapäiväiseen käyttöön

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Group-In: Group Inference from Wireless Traces of Mobile Devices

This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.Comment: This work has been funded by the EU Horizon 2020 Programme under Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The content of this paper does not reflect the official opinion of the EU. Responsibility for the information and views expressed therein lies entirely with the authors. Proc. of ACM/IEEE IPSN'20, 202

A survey of deep learning approaches for WiFi-based indoor positioning

One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a few metres of accuracy on average. In recent years, deep learning has emerged as an alternative approach, with a large number of publications reporting sub-metre positioning accuracy. Therefore, this survey presents a timely, comprehensive review of the most interesting deep learning methods being used for WiFi fingerprinting. In doing so, we aim to identify the most efficient neural networks, under a variety of positioning evaluation metrics for different readers. We will demonstrate that despite the new emerging WiFi signal measures (i.e. CSI and RTT), RSS produces competitive performances under deep learning. We will also show that simple neural networks outperform more complex ones in certain environments

A realistic evaluation of indoor positioning systems based on Wi-Fi fingerprinting: The 2015 EvAAL–ETRI competition

Pre-print versionThis paper presents results from comparing different Wi-Fi fingerprinting algorithms on the same private dataset. The algorithms where realized by independent teams in the frame of the off-site track of the EvAAL-ETRI Indoor Localization Competition which was part of the Sixth International Conference on Indoor Positioning and Indoor Navigation (IPIN 2015). Competitors designed and validated their algorithms against the publicly available UJIIndoorLoc database which contains a huge reference- and validation data set. All competing systems were evaluated using the mean error in positioning, with penalties, using a private test dataset. The authors believe that this is the first work in which Wi-Fi fingerprinting algorithm results delivered by several independent and competing teams are fairly compared under the same evaluation conditions. The analysis also comprises a combined approach: Results indicate that the competing systems where complementary, since an ensemble that combines three competing methods reported the overall best results.We would like to thank Francesco Potortì, Paolo Barsocchi, Michele Girolami and Kyle O’Keefe for their valuable help in organizing and spread the EVAALETRI competition and the off-site track. We would also like to thank the TPC members Machaj Juraj, Christos Laoudias, Antoni Pérez-Navarro and Robert Piché for their valuable comments, suggestions and reviews. Parts of this work were funded in the frame of the Spanish Ministry of Economy and Competitiveness through the “Metodologiías avanzadas para el diseño, desarrollo, evaluación e integración de algoritmos de localización en interiores” project (Proyectos I+D Excelencia, código TIN2015-70202-P) and the “Red de Posicionamiento y Navegación en Interiores” network (Redes de Excelencia, código TEC2015-71426- REDT). Parts of this work were funded in the frame of the German federal Ministry of Education and Research programme "FHprofUnt2013" under contract 03FH035PB3 (Project SPIRIT).info:eu-repo/semantics/acceptedVersio