310 research outputs found
Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey
Copyright © 2013 Jun Hu 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.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Energy Efficient Spectrum Sensing for State Estimation over A Wireless Channel
The performance of remote estimation over wireless channel is strongly
affected by sensor data losses due to interference. Although the impact of
interference can be alleviated by performing spectrum sensing and then
transmitting only when the channel is clear, the introduction of spectrum
sensing also incurs extra energy expenditure. In this paper, we investigate the
problem of energy efficient spectrum sensing for state estimation of a general
linear dynamic system, and formulate an optimization problem which minimizes
the total sensor energy consumption while guaranteeing a desired level of
estimation performance. The optimal solution is evaluated through both
analytical and simulation results.Comment: 4 pages, 6 figures, accepted to IEEE GlobalSIP 201
On Kalman smoothing with random packet loss
The abstract is included in the text
On Kalman Filtering over Fading Wireless Channels with Controlled Transmission Powers
We study stochastic stability of centralized Kalman filtering for linear time-varying systems equipped with wireless sensors. Transmission is over fading channels where variable channel gains are counteracted by power control to alleviate the effects of packet drops. We establish sufficient conditions for the expected value of the Kalman filter covariance matrix to be exponentially bounded in norm. The conditions obtained are then used to formulate stabilizing power control policies which minimize the total sensor power budget. In deriving the optimal power control laws, both statistical channel information and full channel information are considered. The effect of system instability on the power budget is also investigated for both these cases
Optimal Stationary State Estimation Over Multiple Markovian Packet Drop Channels
In this paper, we investigate the state estimation problem over multiple
Markovian packet drop channels. In this problem setup, a remote estimator
receives measurement data transmitted from multiple sensors over individual
channels. By the method of Markovian jump linear systems, an optimal stationary
estimator that minimizes the error variance in the steady state is obtained,
based on the mean-square (MS) stabilizing solution to the coupled algebraic
Riccati equations. An explicit necessary and sufficient condition is derived
for the existence of the MS stabilizing solution, which coincides with that of
the standard Kalman filter. More importantly, we provide a sufficient condition
under which the MS detectability with multiple Markovian packet drop channels
can be decoupled, and propose a locally optimal stationary estimator but
computationally more tractable. Analytic sufficient and necessary MS
detectability conditions are presented for the decoupled subsystems
subsequently. Finally, numerical simulations are conducted to illustrate the
results on the MS stabilizing solution, the MS detectability, and the
performance of the optimal and locally optimal stationary estimators
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
Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systems
In many Cyber-Physical Systems, we encounter the problem of remote state
estimation of geographically distributed and remote physical processes. This
paper studies the scheduling of sensor transmissions to estimate the states of
multiple remote, dynamic processes. Information from the different sensors have
to be transmitted to a central gateway over a wireless network for monitoring
purposes, where typically fewer wireless channels are available than there are
processes to be monitored. For effective estimation at the gateway, the sensors
need to be scheduled appropriately, i.e., at each time instant one needs to
decide which sensors have network access and which ones do not. To address this
scheduling problem, we formulate an associated Markov decision process (MDP).
This MDP is then solved using a Deep Q-Network, a recent deep reinforcement
learning algorithm that is at once scalable and model-free. We compare our
scheduling algorithm to popular scheduling algorithms such as round-robin and
reduced-waiting-time, among others. Our algorithm is shown to significantly
outperform these algorithms for many example scenarios
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