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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
Multiscale Granger causality
In the study of complex physical and biological systems represented by
multivariate stochastic processes, an issue of great relevance is the
description of the system dynamics spanning multiple temporal scales. While
methods to assess the dynamic complexity of individual processes at different
time scales are well-established, multiscale analysis of directed interactions
has never been formalized theoretically, and empirical evaluations are
complicated by practical issues such as filtering and downsampling. Here we
extend the very popular measure of Granger causality (GC), a prominent tool for
assessing directed lagged interactions between joint processes, to quantify
information transfer across multiple time scales. We show that the multiscale
processing of a vector autoregressive (AR) process introduces a moving average
(MA) component, and describe how to represent the resulting ARMA process using
state space (SS) models and to combine the SS model parameters for computing
exact GC values at arbitrarily large time scales. We exploit the theoretical
formulation to identify peculiar features of multiscale GC in basic AR
processes, and demonstrate with numerical simulations the much larger
estimation accuracy of the SS approach compared with pure AR modeling of
filtered and downsampled data. The improved computational reliability is
exploited to disclose meaningful multiscale patterns of information transfer
between global temperature and carbon dioxide concentration time series, both
in paleoclimate and in recent years
Localisation of mobile nodes in wireless networks with correlated in time measurement noise.
Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated
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