1,383 research outputs found
Target Tracking in Confined Environments with Uncertain Sensor Positions
To ensure safety in confined environments such as mines or subway tunnels, a
(wireless) sensor network can be deployed to monitor various environmental
conditions. One of its most important applications is to track personnel,
mobile equipment and vehicles. However, the state-of-the-art algorithms assume
that the positions of the sensors are perfectly known, which is not necessarily
true due to imprecise placement and/or dropping of sensors. Therefore, we
propose an automatic approach for simultaneous refinement of sensors' positions
and target tracking. We divide the considered area in a finite number of cells,
define dynamic and measurement models, and apply a discrete variant of belief
propagation which can efficiently solve this high-dimensional problem, and
handle all non-Gaussian uncertainties expected in this kind of environments.
Finally, we use ray-tracing simulation to generate an artificial mine-like
environment and generate synthetic measurement data. According to our extensive
simulation study, the proposed approach performs significantly better than
standard Bayesian target tracking and localization algorithms, and provides
robustness against outliers.Comment: IEEE Transactions on Vehicular Technology, 201
Opportunistic timing signals for pervasive mobile localization
Mención Internacional en el tÃtulo de doctorThe proliferation of handheld devices and the pressing need of location-based services call for
precise and accurate ubiquitous geographic mobile positioning that can serve a vast set of devices.
Despite the large investments and efforts in academic and industrial communities, a pin-point solution
is however still far from reality. Mobile devices mainly rely on Global Navigation Satellite
System (GNSS) to position themselves. GNSS systems are known to perform poorly in dense urban
areas and indoor environments, where the visibility of GNSS satellites is reduced drastically.
In order to ensure interoperability between the technologies used indoor and outdoor, a pervasive
positioning system should still rely on GNSS, yet complemented with technologies that can
guarantee reliable radio signals in indoor scenarios. The key fact that we exploit is that GNSS signals
are made of data with timing information. We then investigate solutions where opportunistic
timing signals can be extracted out of terrestrial technologies. These signals can then be used as
additional inputs of the multi-lateration problem. Thus, we design and investigate a hybrid system
that combines range measurements from the Global Positioning System (GPS), the world’s
most utilized GNSS system, and terrestrial technologies; the most suitable one to consider in our
investigation is WiFi, thanks to its large deployment in indoor areas. In this context, we first start
investigating standalone WiFi Time-of-flight (ToF)-based localization. Time-of-flight echo techniques
have been recently suggested for ranging mobile devices overWiFi radios. However, these
techniques have yielded only moderate accuracy in indoor environments because WiFi ToF measurements
suffer from extensive device-related noise which makes it challenging to differentiate
between direct path from non-direct path signal components when estimating the ranges. Existing
multipath mitigation techniques tend to fail at identifying the direct path when the device-related
Gaussian noise is in the same order of magnitude, or larger than the multipath noise. In order to
address this challenge, we propose a new method for filtering ranging measurements that is better
suited for the inherent large noise as found in WiFi radios. Our technique combines statistical
learning and robust statistics in a single filter. The filter is lightweight in the sense that it does not
require specialized hardware, the intervention of the user, or cumbersome on-site manual calibration.
This makes the method we propose as the first contribution of the present work particularly
suitable for indoor localization in large-scale deployments using existing legacy WiFi infrastructures.
We evaluate our technique for indoor mobile tracking scenarios in multipath environments,
and, through extensive evaluations across four different testbeds covering areas up to 1000m2, the filter is able to achieve a median ranging error between 1:7 and 2:4 meters.
The next step we envisioned towards preparing theoretical and practical basis for the aforementioned
hybrid positioning system is a deep inspection and investigation of WiFi and GPS ToF
ranges, and initial foundations of single-technology self-localization. Self-localization systems
based on the Time-of-Flight of radio signals are highly susceptible to noise and their performance
therefore heavily rely on the design and parametrization of robust algorithms. We study the noise
sources of GPS and WiFi ToF ranging techniques and compare the performance of different selfpositioning
algorithms at a mobile node using those ranges. Our results show that the localization
error varies greatly depending on the ranging technology, algorithm selection, and appropriate
tuning of the algorithms. We characterize the localization error using real-world measurements
and different parameter settings to provide guidance for the design of robust location estimators
in realistic settings.
These tools and foundations are necessary to tackle the problem of hybrid positioning system
providing high localization capabilities across indoor and outdoor environments. In this context,
the lack of a single positioning system that is able the fulfill the specific requirements of
diverse indoor and outdoor applications settings has led the development of a multitude of localization
technologies. Existing mobile devices such as smartphones therefore commonly rely on
a multi-RAT (Radio Access Technology) architecture to provide pervasive location information
in various environmental contexts as the user is moving. Yet, existing multi-RAT architectures
consider the different localization technologies as monolithic entities and choose the final navigation
position from the RAT that is foreseen to provide the highest accuracy in the particular
context. In contrast, we propose in this work to fuse timing range (Time-of-Flight) measurements
of diverse radio technologies in order to circumvent the limitations of the individual radio access
technologies and improve the overall localization accuracy in different contexts. We introduce
an Extended Kalman filter, modeling the unique noise sources of each ranging technology. As a
rich set of multiple ranges can be available across different RATs, the intelligent selection of the
subset of ranges with accurate timing information is critical to achieve the best positioning accuracy.
We introduce a novel geometrical-statistical approach to best fuse the set of timing ranging
measurements. We also address practical problems of the design space, such as removal of WiFi
chipset and environmental calibration to make the positioning system as autonomous as possible.
Experimental results show that our solution considerably outperforms the use of monolithic
technologies and methods based on classical fault detection and identification typically applied in
standalone GPS technology.
All the contributions and research questions described previously in localization and positioning
related topics suppose full knowledge of the anchors positions. In the last part of this work, we
study the problem of deriving proximity metrics without any prior knowledge of the positions of
the WiFi access points based on WiFi fingerprints, that is, tuples of WiFi Access Points (AP) and
respective received signal strength indicator (RSSI) values. Applications that benefit from proximity
metrics are movement estimation of a single node over time, WiFi fingerprint matching for localization systems and attacks on privacy. Using a large-scale, real-world WiFi fingerprint data
set consisting of 200,000 fingerprints resulting from a large deployment of wearable WiFi sensors,
we show that metrics from related work perform poorly on real-world data. We analyze the
cause for this poor performance, and show that imperfect observations of APs with commodity
WiFi clients in the neighborhood are the root cause. We then propose improved metrics to provide
such proximity estimates, without requiring knowledge of location for the observed AP. We
address the challenge of imperfect observations of APs in the design of these improved metrics.
Our metrics allow to derive a relative distance estimate based on two observed WiFi fingerprints.
We demonstrate that their performance is superior to the related work metrics.This work has been supported by IMDEA Networks InstitutePrograma Oficial de Doctorado en IngenierÃa TelemáticaPresidente: Francisco Barceló Arroyo.- Secretario: Paolo Casari.- Vocal: Marco Fior
Multi-mode Tracking of a Group of Mobile Agents
We consider the problem of tracking a group of mobile nodes with limited
available computational and energy resources given noisy RSSI measurements and
position estimates from group members. The multilateration solutions are known
for energy efficiency. However, these solutions are not directly applicable to
dynamic grouping scenarios where neighbourhoods and resource availability may
frequently change. Existing algorithms such as cluster-based GPS duty-cycling,
individual-based tracking, and multilateration-based tracking can only
partially deal with the challenges of dynamic grouping scenarios. To cope with
these challenges in an effective manner, we propose a new group-based
multi-mode tracking algorithm. The proposed algorithm takes the topological
structure of the group as well as the availability of the resources into
consideration and decides the best solution at any particular time instance. We
consider a clustering approach where a cluster head coordinates the usage of
resources among the cluster members. We evaluate the energy-accuracy trade-off
of the proposed algorithm for various fixed sampling intervals. The evaluation
is based on the 2D position tracks of 40 nodes generated using Reynolds'
flocking model. For a given energy budget, the proposed algorithm reduces the
mean tracking error by up to in comparison to the existing
energy-efficient cooperative algorithms. Moreover, the proposed algorithm is as
accurate as the individual-based tracking while using almost half the energy.Comment: Accepted for publication in the 20th international symposium on
wireless personal multimedia communications (WPMC-2017
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
A multi-hypothesis approach for range-only simultaneous localization and mapping with aerial robots
Los sistemas de Range-only SLAM (o RO-SLAM) tienen como objetivo la construcción de un mapa formado por la posición de un conjunto de sensores de distancia y la localización simultánea del robot con respecto a dicho mapa, utilizando únicamente para ello medidas de distancia. Los sensores de distancia son dispositivos capaces de medir la distancia relativa entre cada par de dispositivos. Estos sensores son especialmente interesantes para su applicación a vehÃculos aéreos debido a su reducido tamaño y peso. Además, estos dispositivos son capaces de operar en interiores o zonas con carencia de señal GPS y no requieren de una lÃnea de visión directa entre cada par de dispositivos a diferencia de otros sensores como cámaras o sensores laser, permitiendo asà obtener una lectura de datos continuada sin oclusiones. Sin embargo, estos sensores presentan un modelo de observación no lineal con una deficiencia de rango debido a la carencia de información de orientación relativa entre cada par de sensores. Además, cuando se incrementa la dimensionalidad del problema de 2D a 3D para su aplicación a vehÃculos aéreos, el número de variables ocultas del modelo aumenta haciendo el problema más costoso computacionalmente especialmente ante implementaciones multi-hipótesis. Esta tesis estudia y propone diferentes métodos que permitan la aplicación eficiente de estos sistemas RO-SLAM con vehÃculos terrestres o aéreos en entornos reales. Para ello se estudia la escalabilidad del sistema en relación al número de variables ocultas y el número de dispositivos a posicionar en el mapa. A diferencia de otros métodos descritos en la literatura de RO-SLAM, los algoritmos propuestos en esta tesis tienen en cuenta las correlaciones existentes entre cada par de dispositivos especialmente para la integración de medidas estÃaË›ticas entre pares de sensores del mapa. Además, esta tesis estudia el ruido y las medidas espúreas que puedan generar los sensores de distancia para mejorar la robustez de los algoritmos propuestos con técnicas de detección y filtración. También se proponen métodos de integración de medidas de otros sensores como cámaras, altÃmetros o GPS para refinar las estimaciones realizadas por el sistema RO-SLAM. Otros capÃtulos estudian y proponen técnicas para la integración de los algoritmos RO-SLAM presentados a sistemas con múltiples robots, asà como el uso de técnicas de percepción activa que permitan reducir la incertidumbre del sistema ante trayectorias con carencia de trilateración entre el robot y los sensores de destancia estáticos del mapa. Todos los métodos propuestos han sido validados mediante simulaciones y experimentos con sistemas reales detallados en esta tesis. Además, todos los sistemas software implementados, asà como los conjuntos de datos registrados durante la experimentación han sido publicados y documentados para su uso en la comunidad cientÃfica
ML-based Approaches for Wireless NLOS Localization: Input Representations and Uncertainty Estimation
The challenging problem of non-line-of-sight (NLOS) localization is critical
for many wireless networking applications. The lack of available datasets has
made NLOS localization difficult to tackle with ML-driven methods, but recent
developments in synthetic dataset generation have provided new opportunities
for research. This paper explores three different input representations: (i)
single wireless radio path features, (ii) wireless radio link features
(multi-path), and (iii) image-based representations. Inspired by the two latter
new representations, we design two convolutional neural networks (CNNs) and we
demonstrate that, although not significantly improving the NLOS localization
performance, they are able to support richer prediction outputs, thus allowing
deeper analysis of the predictions. In particular, the richer outputs enable
reliable identification of non-trustworthy predictions and support the
prediction of the top-K candidate locations for a given instance. We also
measure how the availability of various features (such as angles of signal
departure and arrival) affects the model's performance, providing insights
about the types of data that should be collected for enhanced NLOS
localization. Our insights motivate future work on building more efficient
neural architectures and input representations for improved NLOS localization
performance, along with additional useful application features.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessible. Work partly supported by the RA Science Committee grant
No. 22rl-052 (DISTAL) and the EU under Italian National Recovery and
Resilience Plan of NextGenerationEU on "Telecommunications of the Future"
(PE00000001 - program "RESTART"
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