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references.bib
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@article{Mathworks2018,
author = {Mathworks, Contact},
file = {:Users/jesusoroya/Desktop/fusion{\_}gs.pdf:pdf},
keywords = {MATLAB},
title = {{Sensor Fusion and Tracking Toolbox ™ Getting Started Guide R 2018 b}},
url = {https://es.mathworks.com/help/fusion/},
year = {2018}
}
@article{Correa2017,
abstract = {In the last decade, the interest in Indoor Location Based Services (ILBS) has increased stimulating the development of Indoor Positioning Systems (IPS). In particular, ILBS look for positioning systems that can be applied anywhere in the world for millions of users, that is, there is a need for developing IPS for mass market applications. Those systems must provide accurate position estimations with minimum infrastructure cost and easy scalability to different environments. This survey overviews the current state of the art of IPSs and classifies them in terms of the infrastructure and methodology employed. Finally, each group is reviewed analysing its advantages and disadvantages and its applicability to mass market applications. Keywords:},
author = {Correa, Alejandro and Barcelo, Marc and Morell, Antoni and Vicario, Jose Lopez},
doi = {10.3390/s17081927},
file = {:Users/jesusoroya/Desktop/TFM/biblografia-TFM/sensors-17-01927.pdf:pdf},
issn = {14248220},
journal = {Sensors (Switzerland)},
keywords = {Indoor localization,Indoor location based services,Navigation,Pedestrian tracking},
number = {8},
pmid = {28829386},
title = {{A review of pedestrian indoor positioning systems for mass market applications}},
volume = {17},
year = {2017}
}
@article{Zanella2016,
abstract = {—The term " ranging " is often used to indicate the operations that make it possible to estimate the distance between two nodes by processing some signals generated and/or received by the nodes. In wireless systems, a very popular ranging method makes use of the Radio Signal Strength (RSS), which is a measure of the received radio signal power. However, RSS-based ranging is considered very inaccurate, particularly in indoor environments, mainly because of the randomness of the received signal power. In this tutorial paper, we provide an in-depth analysis of the main factors that affect the variability of the received signal power and the accuracy of the RSS measurements. Starting from a survey of the most common and widely accepted models for the radio signal propagation and the RSS-based ranging, we then focus our attention on some technological and proce-dural pitfalls that are often overlooked, but may significantly affect the accuracy of the RSS-based ranging, and we suggest possible techniques to alleviate such problems. The theoretical argumentation is backed up by a set of empirical results in different scenarios. We conclude the paper by providing some best-practice recommendations for proper RSS-based ranging estimation in wireless networks and discussing new approaches and open research challenges.},
author = {Zanella, Andrea},
doi = {10.1109/COMST.2016.2553452},
file = {:Users/jesusoroya/Desktop/TFM/biblografia-TFM/RSS-pdf.pdf:pdf},
issn = {1553877X},
journal = {IEEE Communications Surveys and Tutorials},
keywords = {802.15.4,Communications technology,Indoor,Localization,Measurements,Outdoor,RSS,RSSI,Radio signal strength,Ranging,Zigbee},
number = {4},
pages = {2662--2686},
title = {{Best practice in RSS measurements and ranging}},
volume = {18},
year = {2016}
}
@article{Jankowski2018,
author = {Jankowski, Tomasz and Nikodem, Maciej},
doi = {10.1109/IPIN.2018.8533754},
file = {:Users/jesusoroya/Library/Containers/com.apple.mail/Data/Library/Mail Downloads/7C0AA575-2078-4D2C-A6CC-A4C63062C581/212651.pdf:pdf},
isbn = {9781538656358},
journal = {2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
keywords = {algorithms,indoor,localization,positioning,simu-},
number = {September},
pages = {24--27},
title = {{SMILe – Simulator for Methods of Indoor Localization}},
year = {2018}
}
@article{Amiot2013,
abstract = {PyLayers is a new open source radio simulator. It has been designed to evaluate localization algorithm performances through the realistic simulation of location-dependent parameters (LDPs) in heterogeneous mobile radio networks. The radio channel is synthesized by using a novel graph-based ray tracing method which has been introduced in order to improve performances in mobile ray-tracing scenarios where geometrical information reuse from one mechanical time-step to another is advantageous. PyLayers can synthesized the narrow band, wide band or ultra wide band (UWB) channel impulse response and thus allows to produced various kind of location dependent parameters as the widely used LDPs received power an time of arrival. Realistic movement of pedestrian agents into the building layout is modeled with a virtual forces approach. The simulated data can be directly exploited with one of the original built-in localization algorithms or be exported to various standards file extensions for external post-processing. Examples of typical PyLayers outputs are provided.},
author = {Amiot, Nicolas and Laaraiedh, Mohamed and Uguen, Bernard},
doi = {10.1109/ICCW.2013.6649206},
file = {:Users/jesusoroya/Downloads/23-PyLayersAnOpenSourceDynamicSimulatorforIndoorPropagationandLocalization.pdf:pdf},
isbn = {9781467357531},
journal = {2013 IEEE International Conference on Communications Workshops, ICC 2013},
keywords = {Simulator,graph theory,localization,mobility model,multi-wall,propagation,ray-tracing},
number = {July 2014},
pages = {84--88},
title = {{PyLayers: An open source dynamic simulator for indoor propagation and localization}},
year = {2013}
}
@article{Seco-Granados2012,
abstract = {Accurately determining ones position has been a recurrent problem in history [1]. It even precedes the first deep-sea navigation attempts of ancient civilizations and reaches the present time with the issue of legal mandates for the location identification of emergency calls in cellular networks and the emergence of location-based services. The science and technology for positioning and navigation has experienced a dramatic evolution [2]. The observation of celestial bodies for navigation purposes has been replaced today by the use of electromagnetic waveforms emitted from reference sources [3] {\textcopyright} 2012 IEEE.},
author = {Seco-Granados, Gonzalo and L{\'{o}}pez-Salcedo, Jos{\'{e}} A. and Jim{\'{e}}nez-Ba{\~{n}}os, David and L{\'{o}}pez-Risueno, Gustavo},
doi = {10.1109/MSP.2011.943410},
file = {:Users/jesusoroya/Library/Application Support/Mendeley Desktop/Downloaded/Seco-Granados et al. - 2012 - Challenges in indoor global navigation satellite systems Unveiling its core features in signal process(28).pdf:pdf},
isbn = {1053-5888},
issn = {10535888},
journal = {IEEE Signal Processing Magazine},
number = {2},
pages = {108--131},
title = {{Challenges in indoor global navigation satellite systems: Unveiling its core features in signal processing}},
url = {http://spcomnav.uab.es/docs/journals/seco{\_}salcedo{\_}IEEE{\_}SPMAG.pdf},
volume = {29},
year = {2012}
}
@article{Diaz2015,
abstract = {Inertial navigation systems use dead-reckoning to estimate the pedestrian's position. There are two types of pedestrian dead-reckoning, the strapdown algorithm and the step-and-heading approach. Unlike the strapdown algorithm, which consists of the double integration of the three orthogonal accelerometer readings, the step-and-heading approach lacks the vertical displacement estimation. We propose the first step-and-heading approach based on unaided inertial data solving 3D positioning. We present a step detector for steps up and down and a novel vertical displacement estimator. Our navigation system uses the sensor introduced in the front pocket of the trousers, a likely location of a smartphone. The proposed algorithms are based on the opening angle of the leg or pitch angle. We analyzed our step detector and compared it with the state-of-the-art, as well as our already proposed step length estimator. Lastly, we assessed our vertical displacement estimator in a real-world scenario. We found that our algorithms outperform the literature step and heading algorithms and solve 3D positioning using unaided inertial data. Additionally, we found that with the pitch angle, five activities are distinguishable: standing, sitting, walking, walking up stairs and walking down stairs. This information complements the pedestrian location and is of interest for applications, such as elderly care.},
author = {Diaz},
doi = {10.3390/s150409156},
file = {:Users/jesusoroya/Desktop/TFM/biblografia-TFM/inertial-tecno.pdf:pdf},
issn = {14248220},
journal = {Sensors (Switzerland)},
keywords = {Activity,Attitude,Dead reckoning,Orientation,Pedestrian,Pitch,Step detector,Step length,Vertical displacement},
number = {4},
pages = {9156--9178},
pmid = {25897501},
title = {{Inertial pocket navigation system: Unaided 3D positioning}},
volume = {15},
year = {2015}
}
@article{Schauer2013,
abstract = {Many existing approaches for WIFI-based indoor positioning systems use the received signal strength indicator (RSSI) of all the access points in reach to estimate the current location of a mobile device. Those systems' accuracy, however, is strongly influenced by external interferences and suffers both from short-term and long-term changes in the respective environments. Time-of-Flight-based (TOF) approaches bypass these problems by relying on the relation between distance and the time it takes a radio signal to travel that distance. Furthermore and in contrast to RSSI-based fingerprinting methods, they require neither a time-consuming calibration phase nor an extensive database. In this paper, we assess the advantages and limitations of TOF-based positioning techniques and compare different approaches in literature in terms of communication flow, hardware components, method of time measurement and positioning accuracy. Additionally, we present a novel approach using NULL-ACK-sequences, off-the-shelf hardware components and the CPU's time stamp counter offering a nanosecond resolution. An association with access points is hence not required and there is no need for modifications of client or infrastructure WIFI-components. We evaluate our system in various settings. Our results indicate that the accuracy of such TOF-based approaches depends on both the used hardware and the characteristics of the given environment. We find that time fluctuations caused by varying delays of the interrupt service routine as well as multipath effects render precise distance estimations based on a single measurement infeasible. In order to obtain stable ranging results we try to minimize these effects by utilizing a high amount of NULL-ACK-sequences. We investigate several filters and statistical estimations and compare them within our system settings. Using a band-pass filter and taking the average of a series of measurements, we are able to achieve a ranging accuracy with a mean ab- olute error of less than 1.33 meters in an ideal environment.},
author = {Schauer, Lorenz and Dorfmeister, Florian and Maier, Marco},
doi = {10.1109/IPIN.2013.6817861},
file = {:Users/jesusoroya/Desktop/TFM/biblografia-TFM/ToFPdf.pdf:pdf},
isbn = {9781479940431},
journal = {2013 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2013},
number = {October},
pages = {28--31},
title = {{Potentials and limitations of WIFI-positioning using time-of-flight}},
year = {2013}
}
@online{Mathworks,
author = {Mathworks},
title = {Sensor Fusion and Tracking Toolbox},
year = 2018,
url = {https://es.mathworks.com/help/fusion/},
urldate = {2018-10-30}
}
@article{Zampella2011,
abstract = {A common problem in the evaluation of Pedestrian Dead Reckoning (PDR) algorithms is the determination of a good ground truth. Some authors propose the use of external motion capture systems, however, their setup, complexity, synchronization and limited coverage are important limitations. We propose the generation of a simulated IMU signal for pedestrians, that is obtained from a given 3D trajectory (position and attitude). The trajectory can be artificially generated or based on a real human walk pattern. This information can be used as a ground truth for the identification of systematic errors, or to obtain a statistical analysis of the effect of any noise added to the simulated signal. Any specific IMU can be simulated by adding its characteristic error pattern, and modifying them, the most influential IMU characteristics can be determined, and if possible minimized. We tested a PDR method based on an Inertial Navigation System (INS) using an Extended Kalman Filter (EKF) with a noiseless IMU signal. Since failures were detected in the stance phase, we proposed and tested some improvements. The influence of adding specific error patterns to the IMU signal were determined measuring their effect on the evolution of the standard deviation of the position error over time. The most influential source of error for an INS mechanization is the bias in the gyroscope, however the EKF-based PDR algorithm showed to diminish in a significant way many of the positioning errors. The IMU-simulation method is proposed as a way to compare several algorithms and to test new PDR improvements during algorithm design.},
author = {Zampella, Francisco J. and Jim{\'{e}}nez, Antonio R. and Seco, Fernando and Prieto, J. Carlos and Guevara, Jorge I.},
doi = {10.1109/IPIN.2011.6071930},
file = {:C$\backslash$:/Users/djoro/Downloads/2011-Simulation of Foot-Mounted IMU Signals for the Evaluation of PDR Algorithms.pdf:pdf},
isbn = {9781457718045},
journal = {2011 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2011},
keywords = {Extended Kalman Filter,IMU simulation,Inertial Navigation,Pedestrian Dead Reckoning},
mendeley-groups = {Indoor Navigation},
title = {{Simulation of foot-mounted IMU signals for the evaluation of PDR algorithms}},
year = {2011}
}
S. Kay, Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory, 1 edition. Englewood Cliffs, N.J: Prentice HallEnglewood Cliffs, N.J: Prentice Hall, 1993.
@book{Kay:1993:FSS:151045,
author = {Kay, Steven M.},
title = {Fundamentals of Statistical Signal Processing: Estimation Theory},
year = {1993},
isbn = {0-13-345711-7},
publisher = {Prentice-Hall, Inc.},
address = {Upper Saddle River, NJ, USA},
}
[download]
@article{UKF,
author = {J. Julier, Simon and K. Uhlmann, Jeffrey},
year = {1999},
month = {02},
pages = {},
title = {A New Extension of the Kalman Filter to Nonlinear Systems},
volume = {3068},
journal = {Proc. SPIE},
doi = {10.1117/12.280797}
}
@misc{navindoor,
author = {DeustoTech},
title = {navindoor-code},
year = {2018},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/DeustoTech/navindoor-code}},
commit = {b3b5f7bac0a0f2f8b15e997b629271be0a9cb6a7}
}