915 research outputs found
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
Pushing towards the Limit of Sampling Rate: Adaptive Chasing Sampling
Measurement samples are often taken in various monitoring applications. To
reduce the sensing cost, it is desirable to achieve better sensing quality
while using fewer samples. Compressive Sensing (CS) technique finds its role
when the signal to be sampled meets certain sparsity requirements. In this
paper we investigate the possibility and basic techniques that could further
reduce the number of samples involved in conventional CS theory by exploiting
learning-based non-uniform adaptive sampling.
Based on a typical signal sensing application, we illustrate and evaluate the
performance of two of our algorithms, Individual Chasing and Centroid Chasing,
for signals of different distribution features. Our proposed learning-based
adaptive sampling schemes complement existing efforts in CS fields and do not
depend on any specific signal reconstruction technique. Compared to
conventional sparse sampling methods, the simulation results demonstrate that
our algorithms allow less number of samples for accurate signal
reconstruction and achieve up to smaller signal reconstruction error
under the same noise condition.Comment: 9 pages, IEEE MASS 201
Applications of Compressive Sampling Technique to Radar and Localization
During the last decade, the emerging technique of compressive sampling (CS) has become a popular subject in signal processing and sensor systems. In particular, CS breaks through the limits imposed by the Nyquist sampling theory and is able to substantially reduce the huge amount of data generated by different sources. The technique of CS has been successfully applied in signal acquisition, image compression, and data reduction. Although the theory of CS has been investigated for some radar and localization problems, several important questions have not been answered yet. For example, the performance of CS radar in a cluttered environment has not been comprehensively studied. Applying CS to passive radars and electronic warfare receivers is another concern that needs more attention. Also, it is well known that applying this strategy leads to extra computational costs which might be prohibitive in large-sized localization networks. In this chapter, we first discuss the practical issues in the process of implementing CS radars and localization systems. Then, we present some promising and efficient solutions to overcome the arising problems
Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach
Global Navigation Satellite Systems typically perform poorly in urban
environments, where the likelihood of line-of-sight conditions between the
devices and the satellites is low, and thus alternative localization methods
are required for good accuracy. We present LocUNet: A convolutional, end-to-end
trained neural network for the localization task, able to estimate the position
of a user from the received signal strength (RSS) from a small number of Base
Stations (BSs). In the proposed method, the user to be localized simply reports
the measured RSS to a central processing unit, which may be located in the
cloud. Using estimations of pathloss radio maps of the BSs and the RSS
measurements, LocUNet can localize users with state-of-the-art accuracy and
enjoys high robustness to inaccuracies in the estimations of radio maps. The
proposed method does not require pre-sampling of new environments and is
suitable for real-time applications. Moreover, two novel datasets that allow
for numerical evaluations of RSS and ToA methods in realistic urban
environments are presented and made publicly available for the research
community. By using these datasets, we also provide a fair comparison of
state-of-the-art RSS and ToA-based methods in the dense urban scenario and show
numerically that LocUNet outperforms all the compared methods.Comment: Submitted to IEEE Transactions on Wireless Communication
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