257 research outputs found
Distributed and adaptive location identification system for mobile devices
Indoor location identification and navigation need to be as simple, seamless,
and ubiquitous as its outdoor GPS-based counterpart is. It would be of great
convenience to the mobile user to be able to continue navigating seamlessly as
he or she moves from a GPS-clear outdoor environment into an indoor environment
or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing
infrastructure-based indoor localization systems lack such capability, on top
of potentially facing several critical technical challenges such as increased
cost of installation, centralization, lack of reliability, poor localization
accuracy, poor adaptation to the dynamics of the surrounding environment,
latency, system-level and computational complexities, repetitive
labor-intensive parameter tuning, and user privacy. To this end, this paper
presents a novel mechanism with the potential to overcome most (if not all) of
the abovementioned challenges. The proposed mechanism is simple, distributed,
adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a
mobile blind device can potentially utilize, as GPS-like reference nodes,
either in-range location-aware compatible mobile devices or preinstalled
low-cost infrastructure-less location-aware beacon nodes. The proposed approach
is model-based and calibration-free that uses the received signal strength to
periodically and collaboratively measure and update the radio frequency
characteristics of the operating environment to estimate the distances to the
reference nodes. Trilateration is then used by the blind device to identify its
own location, similar to that used in the GPS-based system. Simulation and
empirical testing ascertained that the proposed approach can potentially be the
core of future indoor and GPS-obstructed environments
A Novel Approach To Intelligent Navigation Of A Mobile Robot In A Dynamic And Cluttered Indoor Environment
The need and rationale for improved solutions to indoor robot navigation is increasingly driven by the influx of domestic and industrial mobile robots into the market. This research has developed and implemented a novel navigation technique for a mobile robot operating in a cluttered and dynamic indoor environment. It divides the indoor navigation problem into three distinct but interrelated parts, namely, localization, mapping and path planning. The localization part has been addressed using dead-reckoning (odometry). A least squares numerical approach has been used to calibrate the odometer parameters to minimize the effect of systematic errors on the performance, and an intermittent resetting technique, which employs RFID tags placed at known locations in the indoor environment in conjunction with door-markers, has been developed and implemented to mitigate the errors remaining after the calibration. A mapping technique that employs a laser measurement sensor as the main exteroceptive sensor has been developed and implemented for building a binary occupancy grid map of the environment. A-r-Star pathfinder, a new path planning algorithm that is capable of high performance both in cluttered and sparse environments, has been developed and implemented. Its properties, challenges, and solutions to those challenges have also been highlighted in this research. An incremental version of the A-r-Star has been developed to handle dynamic environments. Simulation experiments highlighting properties and performance of the individual components have been developed and executed using MATLAB. A prototype world has been built using the WebotsTM robotic prototyping and 3-D simulation software. An integrated version of the system comprising the localization, mapping and path planning techniques has been executed in this prototype workspace to produce validation results
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
RSS-Based Fusion of UWB and WiFi-Based Ranging for Indoor Positioning
Publisher Copyright: © 2021 CEUR-WS. All rights reserved.WiFi positioning with estimated ranges using Round Trip Time (RTT) measurements based on IEEE 802.11 Wireless Local Area Network (WLAN) has become well known since Fine Timing Measurement (FTM) protocol has been characterized. However, the multipath effect is one of the barriers to accurate time-based range measurement. On the other hand, Ultra Wide Band (UWB)-based range measurement has fair resistance to multipath effects but its accuracy is highly dependant on the orientation of the antennas in the transmitter and the receiver and its transmit power is also limited due to the applied regulations. This paper utilizes a Received Signal Strength (RSS)-based fusion of both UWB and WiFi-based range measurements to increase the indoor positioning accuracy. The proposed method takes the advantage of WiFi FTM protocol as well as Two-Way Ranging (TWR) for UWB devices. The empirical range measurement campaign is done at the University of Helsinki premises. Test points with known positions are considered as the ground truth to evaluate the results. The outcome proves that fusing UWB and WiFi ranges for indoor positioning, improves the accuracy in comparison with using the UWB or WiFi alone.Peer reviewe
AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information
With expeditious development of wireless communications, location
fingerprinting (LF) has nurtured considerable indoor location based services
(ILBSs) in the field of Internet of Things (IoT). For most pattern-matching
based LF solutions, previous works either appeal to the simple received signal
strength (RSS), which suffers from dramatic performance degradation due to
sophisticated environmental dynamics, or rely on the fine-grained physical
layer channel state information (CSI), whose intricate structure leads to an
increased computational complexity. Meanwhile, the harsh indoor environment can
also breed similar radio signatures among certain predefined reference points
(RPs), which may be randomly distributed in the area of interest, thus mightily
tampering the location mapping accuracy. To work out these dilemmas, during the
offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI
amplitude as location fingerprint, which shares the structural simplicity of
RSS while reserving the most location-specific statistical channel information.
Moreover, an additional angle of arrival (AoA) fingerprint can be accurately
retrieved from CSI phase through an enhanced subspace based algorithm, which
serves to further eliminate the error-prone RP candidates. In the online phase,
by exploiting both CSI amplitude and phase information, a novel bivariate
kernel regression scheme is proposed to precisely infer the target's location.
Results from extensive indoor experiments validate the superior localization
performance of our proposed system over previous approaches
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
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