1,388 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
RF Localization in Indoor Environment
In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained
Position Estimation of Robotic Mobile Nodes in Wireless Testbed using GENI
We present a low complexity experimental RF-based indoor localization system
based on the collection and processing of WiFi RSSI signals and processing
using a RSS-based multi-lateration algorithm to determine a robotic mobile
node's location. We use a real indoor wireless testbed called w-iLab.t that is
deployed in Zwijnaarde, Ghent, Belgium. One of the unique attributes of this
testbed is that it provides tools and interfaces using Global Environment for
Network Innovations (GENI) project to easily create reproducible wireless
network experiments in a controlled environment. We provide a low complexity
algorithm to estimate the location of the mobile robots in the indoor
environment. In addition, we provide a comparison between some of our collected
measurements with their corresponding location estimation and the actual robot
location. The comparison shows an accuracy between 0.65 and 5 meters.Comment: (c) 2016 IEEE. Personal use of this material is permitted. Permission
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