35,866 research outputs found
Localization of Multiple Targets with Identical Radar Signatures in Multipath Environments with Correlated Blocking
This paper addresses the problem of localizing an unknown number of targets,
all having the same radar signature, by a distributed MIMO radar consisting of
single antenna transmitters and receivers that cannot determine directions of
departure and arrival. Furthermore, we consider the presence of multipath
propagation, and the possible (correlated) blocking of the direct paths (going
from the transmitter and reflecting off a target to the receiver). In its most
general form, this problem can be cast as a Bayesian estimation problem where
every multipath component is accounted for. However, when the environment map
is unknown, this problem is ill-posed and hence, a tractable approximation is
derived where only direct paths are accounted for. In particular, we take into
account the correlated blocking by scatterers in the environment which appears
as a prior term in the Bayesian estimation framework. A sub-optimal
polynomial-time algorithm to solve the Bayesian multi-target localization
problem with correlated blocking is proposed and its performance is evaluated
using simulations. We found that when correlated blocking is severe, assuming
the blocking events to be independent and having constant probability (as was
done in previous papers) resulted in poor detection performance, with false
alarms more likely to occur than detections.Comment: To appear in the IEEE Transactions on Wireless Communication
Robust Localization of an Arbitrary Distribution of Radioactive Sources for Aerial Inspection
Radiation source detection has seen various applications in the past decade,
ranging from the detection of dirty bombs in public places to scanning critical
nuclear facilities for leakage or flaws, and in the autonomous inspection of
nuclear sites. Despite the success in detecting single point sources or a small
number of spatially separated point sources, most of the existing algorithms
fail to localize sources in complex scenarios with a large number of point
sources or non-trivial distributions & bulk sources. Even in simpler
environments, most existing algorithms are not scalable to larger regions
and/or higher dimensional spaces. For effective autonomous inspection, we not
only need to estimate the positions of the sources, but also the number,
distribution, and intensities of each of them. In this paper, we present a
novel algorithm for the robust localization of an arbitrary distribution of
radiation sources using multi-layer sequential Monte Carlo methods coupled with
suitable clustering algorithms. We achieve near-perfect accuracy, in terms of
F1-scores (> 0.95), while allowing the algorithm to scale, both to large
regions in space and to higher dimensional spaces (5 tested).Comment: 15 pages, 10 figures. Accepted for presentation in Waste Management
Symposium 201
TrackerBots: Autonomous Unmanned Aerial Vehicle for Real-Time Localization and Tracking of Multiple Radio-Tagged Animals
Autonomous aerial robots provide new possibilities to study the habitats and
behaviors of endangered species through the efficient gathering of location
information at temporal and spatial granularities not possible with traditional
manual survey methods. We present a novel autonomous aerial vehicle
system-TrackerBots-to track and localize multiple radio-tagged animals. The
simplicity of measuring the received signal strength indicator (RSSI) values of
very high frequency (VHF) radio-collars commonly used in the field is exploited
to realize a low cost and lightweight tracking platform suitable for
integration with unmanned aerial vehicles (UAVs). Due to uncertainty and the
nonlinearity of the system based on RSSI measurements, our tracking and
planning approaches integrate a particle filter for tracking and localizing; a
partially observable Markov decision process (POMDP) for dynamic path planning.
This approach allows autonomous navigation of a UAV in a direction of maximum
information gain to locate multiple mobile animals and reduce exploration time;
and, consequently, conserve onboard battery power. We also employ the concept
of a search termination criteria to maximize the number of located animals
within power constraints of the aerial system. We validated our real-time and
online approach through both extensive simulations and field experiments with
two mobile VHF radio-tags.Comment: The accepted version to the Journal of Field Robotics, published
after the embargo period (12 months
New Perspectives on Multiple Source Localization in Wireless Sensor Networks
In this paper we address the challenging problem of multiple source
localization in Wireless Sensor Networks (WSN). We develop an efficient
statistical algorithm, based on the novel application of Sequential Monte Carlo
(SMC) sampler methodology, that is able to deal with an unknown number of
sources given quantized data obtained at the fusion center from different
sensors with imperfect wireless channels. We also derive the Posterior
Cram\'er-Rao Bound (PCRB) of the source location estimate. The PCRB is used to
analyze the accuracy of the proposed SMC sampler algorithm and the impact that
quantization has on the accuracy of location estimates of the sources.
Extensive experiments show that the benefits of the proposed scheme in terms of
the accuracy of the estimation method that are required for model selection
(i.e., the number of sources) and the estimation of the source characteristics
compared to the classical importance sampling method
Application of Compressive Sensing Techniques in Distributed Sensor Networks: A Survey
In this survey paper, our goal is to discuss recent advances of compressive
sensing (CS) based solutions in wireless sensor networks (WSNs) including the
main ongoing/recent research efforts, challenges and research trends in this
area. In WSNs, CS based techniques are well motivated by not only the sparsity
prior observed in different forms but also by the requirement of efficient
in-network processing in terms of transmit power and communication bandwidth
even with nonsparse signals. In order to apply CS in a variety of WSN
applications efficiently, there are several factors to be considered beyond the
standard CS framework. We start the discussion with a brief introduction to the
theory of CS and then describe the motivational factors behind the potential
use of CS in WSN applications. Then, we identify three main areas along which
the standard CS framework is extended so that CS can be efficiently applied to
solve a variety of problems specific to WSNs. In particular, we emphasize on
the significance of extending the CS framework to (i). take communication
constraints into account while designing projection matrices and reconstruction
algorithms for signal reconstruction in centralized as well in decentralized
settings, (ii) solve a variety of inference problems such as detection,
classification and parameter estimation, with compressed data without signal
reconstruction and (iii) take practical communication aspects such as
measurement quantization, physical layer secrecy constraints, and imperfect
channel conditions into account. Finally, open research issues and challenges
are discussed in order to provide perspectives for future research directions
Ergodic Exploration of Distributed Information
This paper presents an active search trajectory synthesis technique for
autonomous mobile robots with nonlinear measurements and dynamics. The
presented approach uses the ergodicity of a planned trajectory with respect to
an expected information density map to close the loop during search. The
ergodic control algorithm does not rely on discretization of the search or
action spaces, and is well posed for coverage with respect to the expected
information density whether the information is diffuse or localized, thus
trading off between exploration and exploitation in a single objective
function. As a demonstration, we use a robotic electrolocation platform to
estimate location and size parameters describing static targets in an
underwater environment. Our results demonstrate that the ergodic exploration of
distributed information (EEDI) algorithm outperforms commonly used
information-oriented controllers, particularly when distractions are present.Comment: 17 page
UAVs using Bayesian Optimization to Locate WiFi Devices
We address the problem of localizing non-collaborative WiFi devices in a
large region. Our main motive is to localize humans by localizing their WiFi
devices, e.g. during search-and-rescue operations after a natural disaster. We
use an active sensing approach that relies on Unmanned Aerial Vehicles (UAVs)
to collect signal-strength measurements at informative locations. The problem
is challenging since the measurement is received at arbitrary times and they
are received only when the UAV is in close proximity to the device. For these
reasons, it is extremely important to make prudent decision with very few
measurements. We use the Bayesian optimization approach based on Gaussian
process (GP) regression. This approach works well for our application since GPs
give reliable predictions with very few measurements while Bayesian
optimization makes a judicious trade-off between exploration and exploitation.
In field experiments conducted over a region of 1000 1000 , we
show that our approach reduces the search area to less than 100 meters around
the WiFi device within 5 minutes only. Overall, our approach localizes the
device in less than 15 minutes with an error of less than 20 meters
Hinted Networks
We present Hinted Networks: a collection of architectural transformations for
improving the accuracies of neural network models for regression tasks, through
the injection of a prior for the output prediction (i.e. a hint). We ground our
investigations within the camera relocalization domain, and propose two
variants, namely the Hinted Embedding and Hinted Residual networks, both
applied to the PoseNet base model for regressing camera pose from an image. Our
evaluations show practical improvements in localization accuracy for standard
outdoor and indoor localization datasets, without using additional information.
We further assess the range of accuracy gains within an aerial-view
localization setup, simulated across vast areas at different times of the year
Audio Surveillance: a Systematic Review
Despite surveillance systems are becoming increasingly ubiquitous in our
living environment, automated surveillance, currently based on video sensory
modality and machine intelligence, lacks most of the time the robustness and
reliability required in several real applications. To tackle this issue, audio
sensory devices have been taken into account, both alone or in combination with
video, giving birth, in the last decade, to a considerable amount of research.
In this paper audio-based automated surveillance methods are organized into a
comprehensive survey: a general taxonomy, inspired by the more widespread video
surveillance field, is proposed in order to systematically describe the methods
covering background subtraction, event classification, object tracking and
situation analysis. For each of these tasks, all the significant works are
reviewed, detailing their pros and cons and the context for which they have
been proposed. Moreover, a specific section is devoted to audio features,
discussing their expressiveness and their employment in the above described
tasks. Differently, from other surveys on audio processing and analysis, the
present one is specifically targeted to automated surveillance, highlighting
the target applications of each described methods and providing the reader
tables and schemes useful to retrieve the most suited algorithms for a specific
requirement
Active query-driven visual search using probabilistic bisection and convolutional neural networks
We present a novel efficient object detection and localization framework
based on the probabilistic bisection algorithm. A Convolutional Neural Network
(CNN) is trained and used as a noisy oracle that provides answers to input
query images. The responses along with error probability estimates obtained
from the CNN are used to update beliefs on the object location along each
dimension. We show that querying along each dimension achieves the same lower
bound on localization error as the joint query design. Finally, we compare our
approach to the traditional sliding window technique on a real world face
localization task and show speed improvements by at least an order of magnitude
while maintaining accurate localization.Comment: 5 pages, 4 figures, 1 tabl
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