668 research outputs found
Information fusion architectures for security and resource management in cyber physical systems
Data acquisition through sensors is very crucial in determining the operability of the observed physical entity. Cyber Physical Systems (CPSs) are an example of distributed systems where sensors embedded into the physical system are used in sensing and data acquisition. CPSs are a collaboration between the physical and the computational cyber components. The control decisions sent back to the actuators on the physical components from the computational cyber components closes the feedback loop of the CPS. Since, this feedback is solely based on the data collected through the embedded sensors, information acquisition from the data plays an extremely vital role in determining the operational stability of the CPS. Data collection process may be hindered by disturbances such as system faults, noise and security attacks. Hence, simple data acquisition techniques will not suffice as accurate system representation cannot be obtained. Therefore, more powerful methods of inferring information from collected data such as Information Fusion have to be used.
Information fusion is analogous to the cognitive process used by humans to integrate data continuously from their senses to make inferences about their environment. Data from the sensors is combined using techniques drawn from several disciplines such as Adaptive Filtering, Machine Learning and Pattern Recognition. Decisions made from such combination of data form the crux of information fusion and differentiates it from a flat structured data aggregation. In this dissertation, multi-layered information fusion models are used to develop automated decision making architectures to service security and resource management requirements in Cyber Physical Systems --Abstract, page iv
Intelligent Agents for Disaster Management
ALADDIN [1] is a multi-disciplinary project that is developing novel techniques, architectures, and mechanisms for multi-agent systems in uncertain and dynamic environments. The application focus of the project is disaster management. Research within a number of themes is being pursued and this is considering different aspects of the interaction between autonomous agents and the decentralised system architectures that support those interactions. The aim of the research is to contribute to building more robust multi-agent systems for future applications in disaster management and other similar domains
Cooperative Localization under Limited Connectivity
We report two decentralized multi-agent cooperative localization algorithms
in which, to reduce the communication cost, inter-agent state estimate
correlations are not maintained but accounted for implicitly. In our first
algorithm, to guarantee filter consistency, we account for unknown inter-agent
correlations via an upper bound on the joint covariance matrix of the agents.
In the second method, we use an optimization framework to estimate the unknown
inter-agent cross-covariance matrix. In our algorithms, each agent localizes
itself in a global coordinate frame using a local filter driven by local dead
reckoning and occasional absolute measurement updates, and opportunistically
corrects its pose estimate whenever it can obtain relative measurements with
respect to other mobile agents. To process any relative measurement, only the
agent taken the measurement and the agent the measurement is taken from need to
communicate with each other. Consequently, our algorithms are decentralized
algorithms that do not impose restrictive network-wide connectivity condition.
Moreover, we make no assumptions about the type of agents or relative
measurements. We demonstrate our algorithms in simulation and a
robotic~experiment.Comment: 9 pages, 5 figure
On hybrid consensus-based extended Kalman filtering with random link failures over sensor networks
summary:This paper is concerned with the distributed filtering problem for nonlinear time-varying systems over wireless sensor networks under random link failures. To achieve consensus estimation, each sensor node is allowed to communicate with its neighboring nodes according to a prescribed communication topology. Firstly, a new hybrid consensus-based filtering algorithm under random link failures, which affect the information exchange between sensors and are modeled by a set of independent Bernoulli processes, is designed via redefining the interaction weights. Second, a novel observability condition, called parameterized jointly uniform observability, is proposed to ensure the stochastic boundedness of the error covariances of the hybrid consensus-based filtering algorithm. Finally, an example is given to demonstrate the effectiveness of the derived theoretical results
Doctor of Philosophy
dissertationA safe and secure transportation system is critical to providing protection to those who employ it. Safety is being increasingly demanded within the transportation system and transportation facilities and services will need to adapt to change to provide it. This dissertation provides innovate methodologies to identify current shortcomings and provide theoretic frameworks for enhancing the safety and security of the transportation network. This dissertation is designed to provide multilevel enhanced safety and security within the transportation network by providing methodologies to identify, monitor, and control major hazards associated within the transportation network. The risks specifically addressed are: (1) enhancing nuclear materials sensor networks to better deter and interdict smugglers, (2) use game theory as an interdiction model to design better sensor networks and forensically track smugglers, (3) incorporate safety into regional transportation planning to provide decision-makers a basis for choosing safety design alternatives, and (4) use a simplified car-following model that can incorporate errors to predict situational-dependent safety effects of distracted driving in an ITS infrastructure to deploy live-saving countermeasures
Collaborative Perception From Data Association To Localization
During the last decade, visual sensors have become ubiquitous. One or more cameras
can be found in devices ranging from smartphones to unmanned aerial vehicles and
autonomous cars. During the same time, we have witnessed the emergence of large
scale networks ranging from sensor networks to robotic swarms.
Assume multiple visual sensors perceive the same scene from different viewpoints. In
order to achieve consistent perception, the problem of correspondences between ob-
served features must be first solved. Then, it is often necessary to perform distributed
localization, i.e. to estimate the pose of each agent with respect to a global reference
frame. Having everything set in the same coordinate system and everything having
the same meaning for all agents, operation of the agents and interpretation of the
jointly observed scene become possible.
The questions we address in this thesis are the following: first, can a group of visual
sensors agree on what they see, in a decentralized fashion? This is the problem of
collaborative data association. Then, based on what they see, can the visual sensors
agree on where they are, in a decentralized fashion as well? This is the problem of
cooperative localization.
The contributions of this work are five-fold. We are the first to address the problem
of consistent multiway matching in a decentralized setting. Secondly, we propose
an efficient decentralized dynamical systems approach for computing any number of
smallest eigenvalues and the associated eigenvectors of a weighted graph with global
convergence guarantees with direct applications in group synchronization problems,
e.g. permutations or rotations synchronization. Thirdly, we propose a state-of-the
art framework for decentralized collaborative localization for mobile agents under
the presence of unknown cross-correlations by solving a minimax optimization prob-
lem to account for the missing information. Fourthly, we are the first to present an
approach to the 3-D rotation localization of a camera sensor network from relative
bearing measurements. Lastly, we focus on the case of a group of three visual sensors.
We propose a novel Riemannian geometric representation of the trifocal tensor which
relates projections of points and lines in three overlapping views. The aforemen-
tioned representation enables the use of the state-of-the-art optimization methods on
Riemannian manifolds and the use of robust averaging techniques for estimating the
trifocal tensor
Game Theoretic Training Enabled Deep Learning Solutions for Rapid Discovery of Satellite Behaviors
The chapter presents a game theoretic training model enabling a deep learning solution for rapid discovery of satellite behaviors from collected sensor data. The solution has two parts, namely, Part 1 and Part 2. Part 1 is a PE game model that enables data augmentation method, and Part 2 uses convolutional neural networks (CNNs) for satellite behavior classification. The sensor data are propagated with the various maneuver strategies from the proposed space game models. Under the PE game theoretic framework, various satellite behaviors are simulated to generate synthetic datasets with labels for the training to detect space object behaviors. To evaluate the performance of the proposed PE model, a CNN model is designed and implemented for satellite behavior classification. Python 3 and TensorFlow are used in this implementation. The simulation results show that the trained machine learning model can efficiently and correctly classify the satellite behaviors up to 99.8%
Probabilistic Framework for Sensor Management
A probabilistic sensor management framework is introduced, which maximizes the utility of sensor systems with many different sensing modalities by dynamically configuring the sensor system in the most beneficial way. For this purpose, techniques from stochastic control and Bayesian estimation are combined such that long-term effects of possible sensor configurations and stochastic uncertainties resulting from noisy measurements can be incorporated into the sensor management decisions
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