10,922 research outputs found
An Optimal Transmission Strategy for Kalman Filtering over Packet Dropping Links with Imperfect Acknowledgements
This paper presents a novel design methodology for optimal transmission
policies at a smart sensor to remotely estimate the state of a stable linear
stochastic dynamical system. The sensor makes measurements of the process and
forms estimates of the state using a local Kalman filter. The sensor transmits
quantized information over a packet dropping link to the remote receiver. The
receiver sends packet receipt acknowledgments back to the sensor via an
erroneous feedback communication channel which is itself packet dropping. The
key novelty of this formulation is that the smart sensor decides, at each
discrete time instant, whether to transmit a quantized version of either its
local state estimate or its local innovation. The objective is to design
optimal transmission policies in order to minimize a long term average cost
function as a convex combination of the receiver's expected estimation error
covariance and the energy needed to transmit the packets. The optimal
transmission policy is obtained by the use of dynamic programming techniques.
Using the concept of submodularity, the optimality of a threshold policy in the
case of scalar systems with perfect packet receipt acknowledgments is proved.
Suboptimal solutions and their structural results are also discussed. Numerical
results are presented illustrating the performance of the optimal and
suboptimal transmission policies.Comment: Conditionally accepted in IEEE Transactions on Control of Network
System
Bits About the Channel: Multi-round Protocols for Two-way Fading Channels
Most communication systems use some form of feedback, often related to
channel state information. In this paper, we study diversity multiplexing
tradeoff for both FDD and TDD systems, when both receiver and transmitter
knowledge about the channel is noisy and potentially mismatched. For FDD
systems, we first extend the achievable tradeoff region for 1.5 rounds of
message passing to get higher diversity compared to the best known scheme, in
the regime of higher multiplexing gains. We then break the mold of all current
channel state based protocols by using multiple rounds of conferencing to
extract more bits about the actual channel. This iterative refinement of the
channel increases the diversity order with every round of communication. The
protocols are on-demand in nature, using high powers for training and feedback
only when the channel is in poor states. The key result is that the diversity
multiplexing tradeoff with perfect training and K levels of perfect feedback
can be achieved, even when there are errors in training the receiver and errors
in the feedback link, with a multi-round protocol which has K rounds of
training and K-1 rounds of binary feedback. The above result can be viewed as a
generalization of Zheng and Tse, and Aggarwal and Sabharwal, where the result
was shown to hold for K=1 and K=2 respectively. For TDD systems, we also
develop new achievable strategies with multiple rounds of communication between
the transmitter and the receiver, which use the reciprocity of the forward and
the feedback channel. The multi-round TDD protocol achieves a
diversity-multiplexing tradeoff which uniformly dominates its FDD counterparts,
where no channel reciprocity is available.Comment: Submitted to IEEE Transactions on Information Theor
Recent advances on filtering and control for nonlinear stochastic complex systems with incomplete information: A survey
This Article is provided by the Brunel Open Access Publishing Fund - Copyright @ 2012 Hindawi PublishingSome recent advances on the filtering and control problems for nonlinear stochastic complex systems with incomplete information are surveyed. The incomplete information under consideration mainly includes missing measurements, randomly varying sensor delays, signal quantization, sensor saturations, and signal sampling. With such incomplete information, the developments on various filtering and control issues are reviewed in great detail. In particular, the addressed nonlinear stochastic complex systems are so comprehensive that they include conventional nonlinear stochastic systems, different kinds of complex networks, and a large class of sensor networks. The corresponding filtering and control technologies for such nonlinear stochastic complex systems are then discussed. Subsequently, some latest results on the filtering and control problems for the complex systems with incomplete information are given. Finally, conclusions are drawn and several possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61104125, 61028008, 61174136, 60974030, and 61074129, the Qing Lan Project of Jiangsu Province of China, the Project sponsored by SRF for ROCS of SEM of China, the Engineering and Physical Sciences Research Council EPSRC of the UK under Grant GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
A review on analysis and synthesis of nonlinear stochastic systems with randomly occurring incomplete information
Copyright q 2012 Hongli Dong 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.In the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modeling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent. Such a phenomenon typically appears in a networked environment. Examples include, but are not limited to, randomly occurring uncertainties, randomly occurring nonlinearities, randomly occurring saturation, randomly missing measurements and randomly occurring quantization. Randomly occurring incomplete information, if not properly handled, would seriously deteriorate the performance of a control system. In this paper, we aim to survey some recent advances on the analysis and synthesis problems for nonlinear stochastic systems with randomly occurring incomplete information. The developments of the filtering, control and fault detection problems are systematically reviewed. Latest results on analysis and synthesis of nonlinear stochastic systems are discussed in great detail. In addition, various distributed filtering technologies over sensor networks are highlighted. Finally, some concluding remarks are given and some possible future research directions are pointed out. © 2012 Hongli Dong et al.This work was supported in part by the National Natural Science Foundation of China under Grants 61273156, 61134009, 61273201, 61021002, and 61004067, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK, the National Science Foundation of the USA under Grant No. HRD-1137732, and the Alexander von Humboldt Foundation of German
Dynamic Quantized Consensus of General Linear Multi-agent Systems under Denial-of-Service Attacks
In this paper, we study multi-agent consensus problems under
Denial-of-Service (DoS) attacks with data rate constraints. We first consider
the leaderless consensus problem and after that we briefly present the analysis
of leader-follower consensus. The dynamics of the agents take general forms
modeled as homogeneous linear time-invariant systems. In our analysis, we
derive lower bounds on the data rate for the multi-agent systems to achieve
leaderless and leader-follower consensus in the presence of DoS attacks, under
which the issue of overflow of quantizer is prevented. The main contribution of
the paper is the characterization of the trade-off between the tolerable DoS
attack levels for leaderless and leader-follower consensus and the required
data rates for the quantizers during the communication attempts among the
agents. To mitigate the influence of DoS attacks, we employ dynamic
quantization with zooming-in and zooming-out capabilities for avoiding
quantizer saturation
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