6,733 research outputs found
A decentralized motion coordination strategy for dynamic target tracking
This paper presents a decentralized motion planning
algorithm for the distributed sensing of a noisy dynamical
process by multiple cooperating mobile sensor agents. This
problem is motivated by localization and tracking tasks of
dynamic targets. Our gradient-descent method is based on a
cost function that measures the overall quality of sensing. We
also investigate the role of imperfect communication between
sensor agents in this framework, and examine the trade-offs in
performance between sensing and communication. Simulations
illustrate the basic characteristics of the algorithms
Computation-Communication Trade-offs and Sensor Selection in Real-time Estimation for Processing Networks
Recent advances in electronics are enabling substantial processing to be
performed at each node (robots, sensors) of a networked system. Local
processing enables data compression and may mitigate measurement noise, but it
is still slower compared to a central computer (it entails a larger
computational delay). However, while nodes can process the data in parallel,
the centralized computational is sequential in nature. On the other hand, if a
node sends raw data to a central computer for processing, it incurs
communication delay. This leads to a fundamental communication-computation
trade-off, where each node has to decide on the optimal amount of preprocessing
in order to maximize the network performance. We consider a network in charge
of estimating the state of a dynamical system and provide three contributions.
First, we provide a rigorous problem formulation for optimal real-time
estimation in processing networks in the presence of delays. Second, we show
that, in the case of a homogeneous network (where all sensors have the same
computation) that monitors a continuous-time scalar linear system, the optimal
amount of local preprocessing maximizing the network estimation performance can
be computed analytically. Third, we consider the realistic case of a
heterogeneous network monitoring a discrete-time multi-variate linear system
and provide algorithms to decide on suitable preprocessing at each node, and to
select a sensor subset when computational constraints make using all sensors
suboptimal. Numerical simulations show that selecting the sensors is crucial.
Moreover, we show that if the nodes apply the preprocessing policy suggested by
our algorithms, they can largely improve the network estimation performance.Comment: 15 pages, 16 figures. Accepted journal versio
Mean Square Capacity of Power Constrained Fading Channels with Causal Encoders and Decoders
This paper is concerned with the mean square stabilization problem of
discrete-time LTI systems over a power constrained fading channel. Different
from existing research works, the channel considered in this paper suffers from
both fading and additive noises. We allow any form of causal channel
encoders/decoders, unlike linear encoders/decoders commonly studied in the
literature. Sufficient conditions and necessary conditions for the mean square
stabilizability are given in terms of channel parameters such as transmission
power and fading and additive noise statistics in relation to the unstable
eigenvalues of the open-loop system matrix. The corresponding mean square
capacity of the power constrained fading channel under causal encoders/decoders
is given. It is proved that this mean square capacity is smaller than the
corresponding Shannon channel capacity. In the end, numerical examples are
presented, which demonstrate that the causal encoders/decoders render less
restrictive stabilizability conditions than those under linear
encoders/decoders studied in the existing works.Comment: Accepted by the 54th IEEE Conference on Decision and Contro
Control-theoretic Approach to Communication with Feedback: Fundamental Limits and Code Design
Feedback communication is studied from a control-theoretic perspective,
mapping the communication problem to a control problem in which the control
signal is received through the same noisy channel as in the communication
problem, and the (nonlinear and time-varying) dynamics of the system determine
a subclass of encoders available at the transmitter. The MMSE capacity is
defined to be the supremum exponential decay rate of the mean square decoding
error. This is upper bounded by the information-theoretic feedback capacity,
which is the supremum of the achievable rates. A sufficient condition is
provided under which the upper bound holds with equality. For the special class
of stationary Gaussian channels, a simple application of Bode's integral
formula shows that the feedback capacity, recently characterized by Kim, is
equal to the maximum instability that can be tolerated by the controller under
a given power constraint. Finally, the control mapping is generalized to the
N-sender AWGN multiple access channel. It is shown that Kramer's code for this
channel, which is known to be sum rate optimal in the class of generalized
linear feedback codes, can be obtained by solving a linear quadratic Gaussian
control problem.Comment: Submitted to IEEE Transactions on Automatic Contro
Performance analysis with network-enhanced complexities: On fading measurements, event-triggered mechanisms, and cyber attacks
Copyright © 2014 Derui Ding et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Nowadays, the real-world systems are usually subject to various complexities such as parameter uncertainties, time-delays, and nonlinear disturbances. For networked systems, especially large-scale systems such as multiagent systems and systems over sensor networks, the complexities are inevitably enhanced in terms of their degrees or intensities because of the usage of the communication networks. Therefore, it would be interesting to (1) examine how this kind of network-enhanced complexities affects the control or filtering performance; and (2) develop some suitable approaches for controller/filter design problems. In this paper, we aim to survey some recent advances on the performance analysis and synthesis with three sorts of fashionable network-enhanced complexities, namely, fading measurements, event-triggered mechanisms, and attack behaviors of adversaries. First, these three kinds of complexities are introduced in detail according to their engineering backgrounds, dynamical characteristic, and modelling techniques. Then, the developments of the performance analysis and synthesis issues for various networked systems are systematically reviewed. Furthermore, some challenges are illustrated by using a thorough literature review and some possible future research directions are highlighted.This work was supported in part by the National Natural Science Foundation of China under Grants 61134009, 61329301, 61203139, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
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