1,432 research outputs found
On Communication-Efficient Multisensor Track Association via Measurement Transformation (Extended Version)
Multisensor track-to-track fusion for target tracking involves two primary
operations: track association and estimation fusion. For estimation fusion,
lossless measurement transformation of sensor measurements has been proposed
for single target tracking. In this paper, we investigate track association
which is a fundamental and important problem for multitarget tracking. First,
since the optimal track association problem is a multi-dimensional assignment
(MDA) problem, we demonstrate that MDA-based data association (with and without
prior track information) using linear transformations of track measurements is
lossless, and is equivalent to that using raw track measurements. Second,
recent superior scalability and performance of belief propagation (BP)
algorithms enable new real-time applications of multitarget tracking with
resource-limited devices. Thus, we present a BP-based multisensor track
association method with transformed measurements and show that it is equivalent
to that with raw measurements. Third, considering communication constraints, it
is more beneficial for local sensors to send in compressed data. Two analytical
lossless transformations for track association are provided, and it is shown
that their communication requirements from each sensor to the fusion center are
less than those of fusion with raw track measurements. Numerical examples for
tracking an unknown number of targets verify that track association with
transformed track measurements has the same performance as that with raw
measurements and requires fewer communication bandwidths
A New Approach to Linear/Nonlinear Distributed Fusion Estimation Problem
Disturbance noises are always bounded in a practical system, while fusion
estimation is to best utilize multiple sensor data containing noises for the
purpose of estimating a quantity--a parameter or process. However, few results
are focused on the information fusion estimation problem under bounded noises.
In this paper, we study the distributed fusion estimation problem for linear
time-varying systems and nonlinear systems with bounded noises, where the
addressed noises do not provide any statistical information, and are unknown
but bounded. When considering linear time-varying fusion systems with bounded
noises, a new local Kalman-like estimator is designed such that the square
error of the estimator is bounded as time goes to . A novel
constructive method is proposed to find an upper bound of fusion estimation
error, then a convex optimization problem on the design of an optimal weighting
fusion criterion is established in terms of linear matrix inequalities, which
can be solved by standard software packages. Furthermore, according to the
design method of linear time-varying fusion systems, each local nonlinear
estimator is derived for nonlinear systems with bounded noises by using Taylor
series expansion, and a corresponding distributed fusion criterion is obtained
by solving a convex optimization problem. Finally, target tracking system and
localization of a mobile robot are given to show the advantages and
effectiveness of the proposed methods.Comment: 9 pages, 3 figure
State Estimation with Unconventional and Networked Measurements
This dissertation consists of two main parts. One is about state estimation with two types of unconventional measurements and the other is about two types of network-induced state estimation problems. The two types of unconventional measurements considered are noise-free measurements and set measurements. State estimation with them has numerous real supports. For state estimation with noisy and noise-free measurements, two sequential forms of the batch linear minimum mean-squared error (LMMSE) estimator are obtained to reduce the computational complexity. Inspired by the estimation with quantized measurements developed by Curry [28], under a Gaussian assumption, the minimum mean-squared error (MMSE) state estimator with point measurements and set measurements of any shape is proposed by discretizing continuous set measurements. State estimation under constraints, which are special cases of the more general framework, has some interesting properties. It is found that under certain conditions, although constraints are indispensable in the evolution of the state, update by treating them as measurements is redundant in filtering. The two types of network-induced estimation problems considered are optimal state estimation in the presence of multiple packet dropouts and optimal distributed estimation fusion with transformed data. An alternative form of LMMSE estimation in the presence of multiple packet dropouts, which can overcome the shortcomings of two existing ones, is proposed first. Then under a Gaussian assumption, the MMSE estimation is also obtained based on a hard decision by comparing the measurements at two consecutive time instants. It is pointed out that if this comparison is legitimate, our simple MMSE solution largely nullifies existing work on this problem. By taking linear transformation of the raw measurements received by each sensor, two optimal distributed fusion algorithms are proposed. In terms of optimality, communication and computational requirements, three nice properties make them attractive
Antioxidants: nanotechnology and biotechnology fusion for medicine in overall
Antioxidant is a chemical
substance that is naturally found in our
food. It can prevent or reduce the
oxidative stress of the physiological
system. Due to the regular usage of
oxygen, the body continuously
produces free radicals. Excessive
number of free radicals could cause
cellular damage in the human body that
could lead to various diseases like
cancer, muscular degeneration and
diabetes. The presence of antioxidants
helps to counterattack the effect of
these free radicals. The antioxidant can
be found in abundance in plants and
most of the time there are problems
with the delivery. The solution is by
using nanotechnology that has
multitude potential for advanced
medical science. Nano devices and
nanoparticles have significant impact
as they can interact with the subcellular
level of the body with a high degree of
specificity. Thus, the treatment can be
in maximum efficacy with little side
effect
State Estimation with Unconventional and Networked Measurements
This dissertation consists of two main parts. One is about state estimation with two types of unconventional measurements and the other is about two types of network-induced state estimation problems. The two types of unconventional measurements considered are noise-free measurements and set measurements. State estimation with them has numerous real supports. For state estimation with noisy and noise-free measurements, two sequential forms of the batch linear minimum mean-squared error (LMMSE) estimator are obtained to reduce the computational complexity. Inspired by the estimation with quantized measurements developed by Curry [28], under a Gaussian assumption, the minimum mean-squared error (MMSE) state estimator with point measurements and set measurements of any shape is proposed by discretizing continuous set measurements. State estimation under constraints, which are special cases of the more general framework, has some interesting properties. It is found that under certain conditions, although constraints are indispensable in the evolution of the state, update by treating them as measurements is redundant in filtering. The two types of network-induced estimation problems considered are optimal state estimation in the presence of multiple packet dropouts and optimal distributed estimation fusion with transformed data. An alternative form of LMMSE estimation in the presence of multiple packet dropouts, which can overcome the shortcomings of two existing ones, is proposed first. Then under a Gaussian assumption, the MMSE estimation is also obtained based on a hard decision by comparing the measurements at two consecutive time instants. It is pointed out that if this comparison is legitimate, our simple MMSE solution largely nullifies existing work on this problem. By taking linear transformation of the raw measurements received by each sensor, two optimal distributed fusion algorithms are proposed. In terms of optimality, communication and computational requirements, three nice properties make them attractive
Source Coding in Networks with Covariance Distortion Constraints
We consider a source coding problem with a network scenario in mind, and
formulate it as a remote vector Gaussian Wyner-Ziv problem under covariance
matrix distortions. We define a notion of minimum for two positive-definite
matrices based on which we derive an explicit formula for the rate-distortion
function (RDF). We then study the special cases and applications of this
result. We show that two well-studied source coding problems, i.e. remote
vector Gaussian Wyner-Ziv problems with mean-squared error and mutual
information constraints are in fact special cases of our results. Finally, we
apply our results to a joint source coding and denoising problem. We consider a
network with a centralized topology and a given weighted sum-rate constraint,
where the received signals at the center are to be fused to maximize the output
SNR while enforcing no linear distortion. We show that one can design the
distortion matrices at the nodes in order to maximize the output SNR at the
fusion center. We thereby bridge between denoising and source coding within
this setup
Spatial Statistical Data Fusion on Java-enabled Machines in Ubiquitous Sensor Networks
Wireless Sensor Networks (WSN) consist of small, cheap devices that have a combination of sensing, computing and communication capabilities. They must be able to communicate and process data efficiently using minimum amount of energy and cover an area of interest with the minimum number of sensors. This thesis proposes the use of techniques that were designed for Geostatistics and applies them to WSN field. Kriging and Cokriging interpolation that can be considered as Information Fusion algorithms were tested to prove the feasibility of the methods to increase coverage. To reduce energy consumption, a compression method that models correlations based on variograms was developed. A second challenge is to establish the communication to the external networks and to react to unexpected events. A demonstrator that uses commercial Java-enabled devices was implemented. It is able to perform remote monitoring, send SMS alarms and deploy remote updates
Information Theory and Cooperative Control in Networked Multi-Agent Systems with Applications to Smart Grid
This dissertation focuses on information theoretic aspects of and cooperative control techniques in networked multi-agent systems (NMAS) with communication constraints. In the first part of the dissertation, information theoretic limitations of tracking problems in networked control systems, especially leader-follower systems with communication constraints, are studied. Necessary conditions on the data rate of each communication link for tracking of the leader-follower systems are provided. By considering the forward and feedback channels as one cascade channel, we also provide a lower bound for the data rate of the cascade channel for the system to track a reference signal such that the tracking error has finite second moment. Finally, the aforementioned results are extended to the case in which the leader system and follower system have different system models. In the second part, we propose an easily scalable hierarchical decision-making and control architecture for smart grid with communication constraints in which distributed customers equipped with renewable distributed generation (RDG) interact and trade energy in the grid. We introduce the key components and their interactions in the proposed control architecture and discuss the design of distributed controllers which deal with short-term and long-term grid stability, power load balancing and energy routing. At microgrid level, under the assumption of user cooperation and inter-user communications, we propose a distributed networked control strategy to solve the demand-side management problem in microgrids. Moreover, by considering communication delays between users and microgrid central controller, we propose a distributed networked control strategy with prediction to solve the demand-side management problem with communication delays. In the third part, we consider the disturbance attenuation and stabilization problem in networked control systems. To be specific, we consider the string stability in a large group of interconnected systems over a communication network. Its potential applications could be found in formation tracking control in groups of robots, as well as uncertainty reduction and disturbance attenuation in smart grid. We propose a leader-following consensus protocol for such interconnected systems and derive the sufficient conditions, in terms of communication topology and control parameters, for string stability. Simulation results and performance in terms of disturbance propagation are also given. In the fourth part, we consider distributed tracking and consensus in networked multi-agent systems with noisy time-varying graphs and incomplete data. In particular, a distributed tracking with consensus algorithm is developed for the space-object tracking with a satellite surveillance network. We also intend to investigate the possible application of such methods in smart grid networks. Later, conditions for achieving distributed consensus are discussed and the rate of convergence is quantified for noisy time-varying graphs with incomplete data. We also provide detailed simulation results and performance comparison of the proposed distributed tracking with consensus algorithm in the case of space-object tracking problem and that of distributed local Kalman filtering with centralized fusion and centralized Kalman filter. The information theoretic limitations developed in the first part of this dissertation provide guildlines for design and analysis of tracking problems in networked control systems. The results reveal the mutual interaction and joint application of information theory and control theory in networked control systems. Second, the proposed architectures and approaches enable scalability in smart grid design and allow resource pooling among distributed energy resources (DER) so that the grid stability and optimality is maintained. The proposed distributed networked control strategy with prediction provides an approach for cooperative control at RDG-equipped customers within a self-contained microgrid with different feedback delays. Our string stability analysis in the third part of this dissertation allows a single networked control system to be extended to a large group of interconnected subsystems while system stability is still maintained. It also reveals the disturbance propagation through the network and the effect of disturbance in one subsystem on other subsystems. The proposed leader-following consensus protocol in the constrained communication among users reveals the effect of communication in stabilization of networked control systems and the interaction between communication and control over a network. Finally, the distributed tracking and consensus in networked multi-agent systems problem shows that information sharing among users improves the quality of local estimates and helps avoid conflicting and inefficient distributed decisions. It also reveals the effect of the graph topologies and incomplete node measurements on the speed of achieving distributed decision and final consensus accuracy
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