48,455 research outputs found
Indoor wireless communications and applications
Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter
AROMA: Automatic Generation of Radio Maps for Localization Systems
WLAN localization has become an active research field recently. Due to the
wide WLAN deployment, WLAN localization provides ubiquitous coverage and adds
to the value of the wireless network by providing the location of its users
without using any additional hardware. However, WLAN localization systems
usually require constructing a radio map, which is a major barrier of WLAN
localization systems' deployment. The radio map stores information about the
signal strength from different signal strength streams at selected locations in
the site of interest. Typical construction of a radio map involves measurements
and calibrations making it a tedious and time-consuming operation. In this
paper, we present the AROMA system that automatically constructs accurate
active and passive radio maps for both device-based and device-free WLAN
localization systems. AROMA has three main goals: high accuracy, low
computational requirements, and minimum user overhead. To achieve high
accuracy, AROMA uses 3D ray tracing enhanced with the uniform theory of
diffraction (UTD) to model the electric field behavior and the human shadowing
effect. AROMA also automates a number of routine tasks, such as importing
building models and automatic sampling of the area of interest, to reduce the
user's overhead. Finally, AROMA uses a number of optimization techniques to
reduce the computational requirements. We present our system architecture and
describe the details of its different components that allow AROMA to achieve
its goals. We evaluate AROMA in two different testbeds. Our experiments show
that the predicted signal strength differs from the measurements by a maximum
average absolute error of 3.18 dBm achieving a maximum localization error of
2.44m for both the device-based and device-free cases.Comment: 14 pages, 17 figure
Reflection-Aware Sound Source Localization
We present a novel, reflection-aware method for 3D sound localization in
indoor environments. Unlike prior approaches, which are mainly based on
continuous sound signals from a stationary source, our formulation is designed
to localize the position instantaneously from signals within a single frame. We
consider direct sound and indirect sound signals that reach the microphones
after reflecting off surfaces such as ceilings or walls. We then generate and
trace direct and reflected acoustic paths using inverse acoustic ray tracing
and utilize these paths with Monte Carlo localization to estimate a 3D sound
source position. We have implemented our method on a robot with a cube-shaped
microphone array and tested it against different settings with continuous and
intermittent sound signals with a stationary or a mobile source. Across
different settings, our approach can localize the sound with an average
distance error of 0.8m tested in a room of 7m by 7m area with 3m height,
including a mobile and non-line-of-sight sound source. We also reveal that the
modeling of indirect rays increases the localization accuracy by 40% compared
to only using direct acoustic rays.Comment: Submitted to ICRA 2018. The working video is available at
(https://youtu.be/TkQ36lMEC-M
Seismic modeling using the frozen Gaussian approximation
We adopt the frozen Gaussian approximation (FGA) for modeling seismic waves.
The method belongs to the category of ray-based beam methods. It decomposes
seismic wavefield into a set of Gaussian functions and propagates these
Gaussian functions along appropriate ray paths. As opposed to the classic
Gaussian-beam method, FGA keeps the Gaussians frozen (at a fixed width) during
the propagation process and adjusts their amplitudes to produce an accurate
approximation after summation. We perform the initial decomposition of seismic
data using a fast version of the Fourier-Bros-Iagolnitzer (FBI) transform and
propagate the frozen Gaussian beams numerically using ray tracing. A test using
a smoothed Marmousi model confirms the validity of FGA for accurate modeling of
seismic wavefields.Comment: 5 pages, 8 figure
An indoor variance-based localization technique utilizing the UWB estimation of geometrical propagation parameters
A novel localization framework is presented based on ultra-wideband (UWB) channel sounding, employing a triangulation method using the geometrical properties of propagation paths, such as time delay of arrival, angle of departure, angle of arrival, and their estimated variances. In order to extract these parameters from the UWB sounding data, an extension to the high-resolution RiMAX algorithm was developed, facilitating the analysis of these frequency-dependent multipath parameters. This framework was then tested by performing indoor measurements with a vector network analyzer and virtual antenna arrays. The estimated means and variances of these geometrical parameters were utilized to generate multiple sample sets of input values for our localization framework. Next to that, we consider the existence of multiple possible target locations, which were subsequently clustered using a Kim-Parks algorithm, resulting in a more robust estimation of each target node. Measurements reveal that our newly proposed technique achieves an average accuracy of 0.26, 0.28, and 0.90 m in line-of-sight (LoS), obstructed-LoS, and non-LoS scenarios, respectively, and this with only one single beacon node. Moreover, utilizing the estimated variances of the multipath parameters proved to enhance the location estimation significantly compared to only utilizing their estimated mean values
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
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