8,168 research outputs found
Collaborative signal and information processing for target detection with heterogeneous sensor networks
In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield
Reconfiguration of Distributed Information Fusion System ? A case study
Information Fusion Systems are now widely used in different fusion contexts,
like scientific processing, sensor networks, video and image processing. One of
the current trends in this area is to cope with distributed systems. In this
context, we have defined and implemented a Dynamic Distributed Information
Fusion System runtime model. It allows us to cope with dynamic execution
supports while trying to maintain the functionalities of a given Dynamic
Distributed Information Fusion System. The paper presents our system, the
reconfiguration problems we are faced with and our solutions.Comment: 6 pages - Preprint versio
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
Spatial Whitening Framework for Distributed Estimation
Designing resource allocation strategies for power constrained sensor network
in the presence of correlated data often gives rise to intractable problem
formulations. In such situations, applying well-known strategies derived from
conditional-independence assumption may turn out to be fairly suboptimal. In
this paper, we address this issue by proposing an adjacency-based spatial
whitening scheme, where each sensor exchanges its observation with their
neighbors prior to encoding their own private information and transmitting it
to the fusion center. We comment on the computational limitations for obtaining
the optimal whitening transformation, and propose an iterative optimization
scheme to achieve the same for large networks. We demonstrate the efficacy of
the whitening framework by considering the example of bit-allocation for
distributed estimation.Comment: 4 pages, 2 figures, this paper has been presented at CAMSAP 2011;
Proc. 4th Intl. Workshop on Computational Advances in Multi-Sensor Adaptive
Processing (CAMSAP 2011), San Juan, Puerto Rico, Dec 13-16, 201
Cramer-Rao Bounds for Joint RSS/DoA-Based Primary-User Localization in Cognitive Radio Networks
Knowledge about the location of licensed primary-users (PU) could enable
several key features in cognitive radio (CR) networks including improved
spatio-temporal sensing, intelligent location-aware routing, as well as aiding
spectrum policy enforcement. In this paper we consider the achievable accuracy
of PU localization algorithms that jointly utilize received-signal-strength
(RSS) and direction-of-arrival (DoA) measurements by evaluating the Cramer-Rao
Bound (CRB). Previous works evaluate the CRB for RSS-only and DoA-only
localization algorithms separately and assume DoA estimation error variance is
a fixed constant or rather independent of RSS. We derive the CRB for joint
RSS/DoA-based PU localization algorithms based on the mathematical model of DoA
estimation error variance as a function of RSS, for a given CR placement. The
bound is compared with practical localization algorithms and the impact of
several key parameters, such as number of nodes, number of antennas and
samples, channel shadowing variance and correlation distance, on the achievable
accuracy are thoroughly analyzed and discussed. We also derive the closed-form
asymptotic CRB for uniform random CR placement, and perform theoretical and
numerical studies on the required number of CRs such that the asymptotic CRB
tightly approximates the numerical integration of the CRB for a given
placement.Comment: 20 pages, 11 figures, 1 table, submitted to IEEE Transactions on
Wireless Communication
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