2,610 research outputs found
Data fusion strategy for precise vehicle location for intelligent self-aware maintenance systems
Abstract— Nowadays careful measurement applications are
handed over to Wired and Wireless Sensor Network. Taking
the scenario of train location as an example, this would lead to
an increase in uncertainty about position related to sensors
with long acquisition times like Balises, RFID and
Transponders along the track. We take into account the data
without any synchronization protocols, for increase the
accuracy and reduce the uncertainty after the data fusion
algorithms. The case studies, we have analysed, derived from
the needs of the project partners: train localization, head of an
auger in the drilling sector localization and the location of
containers of radioactive material waste in a reprocessing
nuclear plant. They have the necessity to plan the maintenance
operations of their infrastructure basing through architecture
that taking input from the sensors, which are localization and
diagnosis, maps and cost, to optimize the cost effectiveness and
reduce the time of operation
Workshop on multisensor integration in manufacturing automation
Journal ArticleMany people helped make the Workshop a success, but special thanks must be given to Howard Moraff for his support, and to Vicky Jackson for her efforts in making things run smoothly. Finally, thanks to Jake Aggarwal for helping to start the ball rolling
An annotated bibligraphy of multisensor integration
technical reportIn this paper we give an annotated bibliography of the multisensor integration literature
Reduced-Dimension Linear Transform Coding of Correlated Signals in Networks
A model, called the linear transform network (LTN), is proposed to analyze
the compression and estimation of correlated signals transmitted over directed
acyclic graphs (DAGs). An LTN is a DAG network with multiple source and
receiver nodes. Source nodes transmit subspace projections of random correlated
signals by applying reduced-dimension linear transforms. The subspace
projections are linearly processed by multiple relays and routed to intended
receivers. Each receiver applies a linear estimator to approximate a subset of
the sources with minimum mean squared error (MSE) distortion. The model is
extended to include noisy networks with power constraints on transmitters. A
key task is to compute all local compression matrices and linear estimators in
the network to minimize end-to-end distortion. The non-convex problem is solved
iteratively within an optimization framework using constrained quadratic
programs (QPs). The proposed algorithm recovers as special cases the regular
and distributed Karhunen-Loeve transforms (KLTs). Cut-set lower bounds on the
distortion region of multi-source, multi-receiver networks are given for linear
coding based on convex relaxations. Cut-set lower bounds are also given for any
coding strategy based on information theory. The distortion region and
compression-estimation tradeoffs are illustrated for different communication
demands (e.g. multiple unicast), and graph structures.Comment: 33 pages, 7 figures, To appear in IEEE Transactions on Signal
Processin
3D characterization of Magnetic Flux Leakage signals : a data fusion approach
http://www.worldcat.org/oclc/3946080
Logical Control for Mobile Robots
In this work we present a distributed sensor-based control strategy for mobile robot navigation. We investigate a server-client model, where the clients are executing their tasks in parallel. The logical sensor approach is used as a hybrid framework to model and implement the sensory system for control of the mobile robot. The framework allows for a hierarchical data representation scheme, where sensory data and uncertainty is modeled and used at different levels, depending on the nature of the requested control command
Cognitively-Engineered Multisensor Data Fusion Systems for Military Applications
The fusion of imagery from multiple sensors is a field of research that has been gaining prominence in the scientific community in recent years. The technical aspects of combining multisensory information have been and are currently being studied extensively. However, the cognitive aspects of multisensor data fusion have not received so much attention. Prior research in the field of cognitive engineering has shown that the cognitive aspects of any human-machine system should be taken into consideration in order to achieve systems that are both safe and useful. The goal of this research was to model how humans interpret multisensory data, and to evaluate the value of a cognitively-engineered multisensory data fusion system as an effective, time-saving means of presenting information in high- stress situations. Specifically, this research used principles from cognitive engineering to design, implement, and evaluate a multisensor data fusion system for pilots in high-stress situations. Two preliminary studies were performed, and concurrent protocol analysis was conducted to determine how humans interpret and mentally fuse information from multiple sensors in both low- and high-stress environments. This information was used to develop a model for human processing of information from multiple data sources. This model was then implemented in the development of algorithms for fusing imagery from several disparate sensors (visible and infrared). The model and the system as a whole were empirically evaluated in an experiment with fighter pilots in a simulated combat environment. The results show that the model is an accurate depiction of how humans interpret information from multiple disparate sensors, and that the algorithms show promise for assisting fighter pilots in quicker and more accurate target identification
Distributed Bayesian Filtering using Logarithmic Opinion Pool for Dynamic Sensor Networks
The discrete-time Distributed Bayesian Filtering (DBF) algorithm is presented
for the problem of tracking a target dynamic model using a time-varying network
of heterogeneous sensing agents. In the DBF algorithm, the sensing agents
combine their normalized likelihood functions in a distributed manner using the
logarithmic opinion pool and the dynamic average consensus algorithm. We show
that each agent's estimated likelihood function globally exponentially
converges to an error ball centered on the joint likelihood function of the
centralized multi-sensor Bayesian filtering algorithm. We rigorously
characterize the convergence, stability, and robustness properties of the DBF
algorithm. Moreover, we provide an explicit bound on the time step size of the
DBF algorithm that depends on the time-scale of the target dynamics, the
desired convergence error bound, and the modeling and communication error
bounds. Furthermore, the DBF algorithm for linear-Gaussian models is cast into
a modified form of the Kalman information filter. The performance and robust
properties of the DBF algorithm are validated using numerical simulations
- …