An infrastructure-free magnetic-based indoor positioning system with deep learning

POCI-01-0247-FEDER-033479Infrastructure-free Indoor Positioning Systems (IPS) are becoming popular due to their scalability and a wide range of applications. Such systems often rely on deployed Wi-Fi networks. However, their usability may be compromised, either due to scanning restrictions from recent Android versions or the proliferation of 5G technology. This raises the need for new infrastructure-free IPS independent of Wi-Fi networks. In this paper, we propose the use of magnetic field data for IPS, through Deep Neural Networks (DNN). Firstly, a dataset of human indoor trajectories was collected with different smartphones. Afterwards, a magnetic fingerprint was constructed and relevant features were extracted to train a DNN that returns a probability map of a user’s location. Finally, two postprocessing methods were applied to obtain the most probable location regions. We asserted the performance of our solution against a test dataset, which produced a Success Rate of around 80%. We believe that these results are competitive for an IPS based on a single sensing source. Moreover, the magnetic field can be used as an additional information layer to increase the robustness and redundancy of current multi-source IPS.publishersversionpublishe

Deep Learning Methods for Fingerprint-Based Indoor and Outdoor Positioning

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. The contribution of this dissertation is fourfold: First, a Convolutional Neural Network (CNN)-based method for localizing a smartwatch indoors using geomagnetic field measurements is presented. The proposed method was tested on real world data in an indoor environment composed of three corridors of different lengths and three rooms of different sizes. Experimental results show a promising location classification accuracy of 97.77% with a mean localization error of 0.14 meter (m). Second, a method that makes use of cellular signals emitting from a serving eNodeB to provide symbolic indoor positioning is presented. The proposed method utilizes Denoising Autoencoders (DAEs) to mitigate the effects of cellular signal loss. The proposed method was evaluated using real-world data collected from two different smartphones inside a representative apartment of eight symbolic spaces. Experimental results verify that the proposed method outperforms conventional symbolic indoor positioning techniques in various performance metrics. Third, an investigation is conducted to determine whether Variational Autoencoders (VAEs) and Conditional Variational Autoencoders (CVAEs) are able to learn the distribution of the minority symbolic spaces, for a highly imbalanced fingerprinting dataset, so as to generate synthetic fingerprints that promote enhancements in a classifier\u27s performance. Experimental results show that this is indeed the case. By using various performance evaluation metrics, the achieved results are compared to those obtained by two state-of-the-art oversampling methods known as Synthetic Minority Oversampling TEchnique (SMOTE) and ADAptive SYNthetic (ADASYN) sampling. Fourth, a novel dataset of outdoor location fingerprints is presented. The proposed dataset, named OutFin, addresses the lack of publicly available datasets that researchers can use to develop, evaluate, and compare fingerprint-based positioning solutions which can constitute a high entry barrier for studies. OutFin is comprised of diverse data types such as WiFi, Bluetooth, and cellular signal strengths, in addition to measurements from various sensors including the magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor. The collection area spanned four dispersed sites with a total of 122 Reference Points (RPs). Before OutFin was made available to the public, several experiments were conducted to validate its technical quality

Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning

Accurate location information has significant commercial and economic value as they are widely used in intelligent manufacturing, material localization and smart homes. Magnetic sequence-based approaches show great promise mainly due to their pervasiveness and stability. However, existing geomagnetic indoor localization methods are facing the problems of location ambiguity and feature extraction deficiency, which will lead to large localization errors. To address these issues, we propose a coarse-to-fine geomagnetic indoor localization method based on deep learning. First, a multidimensional geomagnetic feature extraction method is presented which can extract magnetic features from spatial and temporal aspects. Then, a hierarchical deep neural network model is devised to extract more accurate geomagnetic information and corresponding location clues for more accurate localization. Finally, localization is achieved through a particle filter combined with IMU localization. To evaluate the performance of the proposed methods, we carried out several experiments at three trial paths with two heterogeneous devices, Vivo X30 and Huawei Mate30. Experimental results demonstrate that the proposed algorithm can achieve more accurate localization performance than the state-of-the-art methods. Meanwhile, the proposed algorithm has low cost and good pervasiveness for different devices

Crowdsourcing-Based Fingerprinting for Indoor Location in Multi-Storey Buildings

POCI-01-0247-FEDER-033479The number of available indoor location solutions has been growing, however with insufficient precision, high implementation costs or scalability limitations. As fingerprinting-based methods rely on ubiquitous information in buildings, the need for additional infrastructure is discarded. Still, the time-consuming manual process to acquire fingerprints limits their applicability in most scenarios. This paper proposes an algorithm for the automatic construction of environmental fingerprints on multi-storey buildings, leveraging the information sources available in each scenario. It relies on unlabelled crowdsourced data from users’ smartphones. With only the floor plans as input, a demand for most applications, we apply a multimodal approach that joins inertial data, local magnetic field andWi-Fi signals to construct highly accurate fingerprints. Precise movement estimation is achieved regardless of smartphone usage through Deep Neural Networks, and the transition between floors detected from barometric data. Users’ trajectories obtained with Pedestrian Dead Reckoning techniques are partitioned into clusters with Wi-Fi measurements. Straight sections from the same cluster are then compared with subsequence Dynamic Time Warping to search for similarities. From the identified overlapping sections, a particle filter fits each trajectory into the building’s floor plans. From all successfully mapped routes, fingerprints labelled with physical locations are finally obtained. Experimental results from an office and a university building show that this solution constructs comparable fingerprints to those acquired manually, thus providing a useful tool for fingerprinting-based solutions automatic setup.publishersversionpublishe

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

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Zero-Shot Multi-View Indoor Localization via Graph Location Networks

Indoor localization is a fundamental problem in location-based applications. Current approaches to this problem typically rely on Radio Frequency technology, which requires not only supporting infrastructures but human efforts to measure and calibrate the signal. Moreover, data collection for all locations is indispensable in existing methods, which in turn hinders their large-scale deployment. In this paper, we propose a novel neural network based architecture Graph Location Networks (GLN) to perform infrastructure-free, multi-view image based indoor localization. GLN makes location predictions based on robust location representations extracted from images through message-passing networks. Furthermore, we introduce a novel zero-shot indoor localization setting and tackle it by extending the proposed GLN to a dedicated zero-shot version, which exploits a novel mechanism Map2Vec to train location-aware embeddings and make predictions on novel unseen locations. Our extensive experiments show that the proposed approach outperforms state-of-the-art methods in the standard setting, and achieves promising accuracy even in the zero-shot setting where data for half of the locations are not available. The source code and datasets are publicly available at https://github.com/coldmanck/zero-shot-indoor-localization-release.Comment: Accepted at ACM MM 2020. 10 pages, 7 figures. Code and datasets available at https://github.com/coldmanck/zero-shot-indoor-localization-releas

An investigation of new ionospheric models using multi-source measurements and neural networks

Ionosphere is one of the atmospheric layers that has a major impact on human beings since it significantly affects the radio propagation on Earth, and between satellites and Earth (e.g., Global Navigation Satellite Systems (GNSS) signal transmission). The variation of the electrons in the ionosphere is strongly influenced by the space weather due to solar and cosmic radiation. Hence, the short/long-term trend of the free electrons in the ionosphere has been regarded as very important information for both space weather and GNSS positioning. On the other hand, precisely quantifying the distribution and variation of free electrons at a high spatio-temporal resolution is often a challenge if the number of the electrons (electron density) is detected only from the traditional ionospheric sensors (e.g., ionosonde and topside sounder and Incoherent Scatter Radar (ISR)) due to their low spatio-temporal coverage. This disadvantage is also inherited from the empirical ionospheric model developed based on these data sources. Nowadays, the availability of advanced observation techniques, such as GNSS Radio Occultation (RO) and satellite altimetry, for the measurement of Electron Density (Ne) and related parameters (e.g., hmF2, NmF2, Vertical Scale Height (VSH), Electron Density Profile (EDP) and Vertical Total Electron Content (VTEC)) in the ionosphere has heralded a new era for space weather research in the upper atmosphere. The new sources of data for ionospheric modelling can improve not only the accuracy but also the reliability of the model (such as[96] for hmF2 and [28] for VTEC). In this study, Helmert Variance Component Estimation (VCE) aided Weight Total Least Squares (WTLS) is selected for modelling global VTEC using International GNSS Service stations, satellite altimetry and GNSS-RO measurements. The results show that the new VTEC model outperforms the traditional global ionospheric VTEC Model by at least 1.5 Total Electron Content Unit (TECU) over the ocean. This improvement is expected to be significant in the refinement of global ionospheric VTEC Model development. As is well known, the most traditional models developed are prone to the effects of inherent assumptions (e.g. for the construction of the base functions in the models) which may lead to large biases in the prediction. In this study, an innovative machine learning technique (i.e. Neural Network (NN)) is investigated as the modelling method to address this issue. Different from the traditional modelling method, neither the observation equations (or the so called `design matrix'), nor apriori knowledge of the relationship (both of them can be considered as the source of the aforementioned assumptions) is required in the modelling process of a NN. This network system can automatically construct an optimal regression function based on a large amount of sample data and the designed network [43]. In this study, Deep Neural Network (DNN), which is an advanced Artificial Neural Network (ANN) (with more than one hidden layer), is investigated for their usability of VSH and topside EDP modelling, as well as the relationship between Ne and electron temperature. The results reveal that the new VSH model agrees better than the traditional model with regards to either out-of-sample measurements or the external reference (i.e. ISR data). In addition, the new model can represent the characteristic of VSH in the equatorial region better than that of traditional approaches during geomagnetic storms. The relationship between Ne and Electron Temperature (Te) investigated from ISR data can be used to improve the performance of the current Te model. The local time-altitude variation of the model outputs agrees well with that from a physical model (i.e., Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM)). The new topside EDP model takes hmF2 and NmF2 into consideration as part of the variable set. Comparing with the reference data (i.e., out- of-sample COSMIC data, GRACE and ISR data), the new model agrees much better than the International Reference Ionosphere (IRI)-2016 model. In addition, an advanced NN technique, Bidirectional Long Short-Term Memory (Bi-LSTM), is utilised to forecast hmF2 by using the hmF2 measured by Australian ionosondes in the five hours prior. The forecast results are better than the results from real-time models in the next five hours. The new model performs also better than the current hmF2 model (i.e., AMTB [2] and shubin [96] models, which is used inside IRI-2016 model) by at least 10km in most ionosonde stations. Overall, the neural network technique has a great potential in being utilised in the ionospheric modelling. In addition to the accuracy improvement, the physical mechanism can be observed from the model outputs as well. In future work, the neural network is expected to be further applied in some other space weather studies (e.g., Dst, solar flare, etc)