1,169 research outputs found
A Robust Zero-Calibration RF-based Localization System for Realistic Environments
Due to the noisy indoor radio propagation channel, Radio Frequency (RF)-based
location determination systems usually require a tedious calibration phase to
construct an RF fingerprint of the area of interest. This fingerprint varies
with the used mobile device, changes of the transmit power of smart access
points (APs), and dynamic changes in the environment; requiring re-calibration
of the area of interest; which reduces the technology ease of use. In this
paper, we present IncVoronoi: a novel system that can provide zero-calibration
accurate RF-based indoor localization that works in realistic environments. The
basic idea is that the relative relation between the received signal strength
from two APs at a certain location reflects the relative distance from this
location to the respective APs. Building on this, IncVoronoi incrementally
reduces the user ambiguity region based on refining the Voronoi tessellation of
the area of interest. IncVoronoi also includes a number of modules to
efficiently run in realtime as well as to handle practical deployment issues
including the noisy wireless environment, obstacles in the environment,
heterogeneous devices hardware, and smart APs. We have deployed IncVoronoi on
different Android phones using the iBeacons technology in a university campus.
Evaluation of IncVoronoi with a side-by-side comparison with traditional
fingerprinting techniques shows that it can achieve a consistent median
accuracy of 2.8m under different scenarios with a low beacon density of one
beacon every 44m2. Compared to fingerprinting techniques, whose accuracy
degrades by at least 156%, this accuracy comes with no training overhead and is
robust to the different user devices, different transmit powers, and over
temporal changes in the environment. This highlights the promise of IncVoronoi
as a next generation indoor localization system.Comment: 9 pages, 13 figures, published in SECON 201
Hybrid Radio-map for Noise Tolerant Wireless Indoor Localization
In wireless networks, radio-map based locating techniques are commonly used
to cope the complex fading feature of radio signal, in which a radio-map is
built by calibrating received signal strength (RSS) signatures at training
locations in the offline phase. However, in severe hostile environments, such
as in ship cabins where severe shadowing, blocking and multi-path fading
effects are posed by ubiquitous metallic architecture, even radio-map cannot
capture the dynamics of RSS. In this paper, we introduced multiple feature
radio-map location method for severely noisy environments. We proposed to add
low variance signature into radio map. Since the low variance signatures are
generally expensive to obtain, we focus on the scenario when the low variance
signatures are sparse. We studied efficient construction of multi-feature
radio-map in offline phase, and proposed feasible region narrowing down and
particle based algorithm for online tracking. Simulation results show the
remarkably performance improvement in terms of positioning accuracy and
robustness against RSS noises than the traditional radio-map method.Comment: 6 pages, 11th IEEE International Conference on Networking, Sensing
and Control, April 7-9, 2014, Miami, FL, US
DILAND: An Algorithm for Distributed Sensor Localization with Noisy Distance Measurements
In this correspondence, we present an algorithm for distributed sensor
localization with noisy distance measurements (DILAND) that extends and makes
the DLRE more robust. DLRE is a distributed sensor localization algorithm in
introduced in \cite{usman_loctsp:08}. DILAND operates
when (i) the communication among the sensors is noisy; (ii) the communication
links in the network may fail with a non-zero probability; and (iii) the
measurements performed to compute distances among the sensors are corrupted
with noise. The sensors (which do not know their locations) lie in the convex
hull of at least anchors (nodes that know their own locations.) Under
minimal assumptions on the connectivity and triangulation of each sensor in the
network, this correspondence shows that, under the broad random phenomena
described above, DILAND converges almost surely (a.s.) to the exact sensor
locations.Comment: Submitted to the IEEE Transactions on Signal Processing. Initial
submission on May 2009. 12 page
Efficient AoA-based wireless indoor localization for hospital outpatients using mobile devices
The motivation of this work is to help outpatients find their corresponding departments or clinics, thus, it needs to provide indoor positioning services with a room-level accuracy. Unlike wireless outdoor localization that is dominated by the global positioning system (GPS), wireless indoor localization is still an open issue. Many different schemes are being developed to meet the increasing demand for indoor localization services. In this paper, we investigated the AoA-based wireless indoor localization for outpatients’ wayfinding in a hospital, where Wi-Fi access points (APs) are deployed, in line, on the ceiling. The target position can be determined by a mobile device, like a smartphone, through an efficient geometric calculation with two known APs coordinates and the angles of the incident radios. All possible positions in which the target may appear have been comprehensively investigated, and the corresponding solutions were proven to be the same. Experimental results show that localization error was less than 2.5 m, about 80% of the time, which can satisfy the outpatients’ requirements for wayfinding
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