1,218 research outputs found
Distributed bounded-error state estimation for partitioned systems based on practical robust positive invariance
We propose a partition-based state estimator for linear discrete-time systems
composed by coupled subsystems affected by bounded disturbances. The
architecture is distributed in the sense that each subsystem is equipped with a
local state estimator that exploits suitable pieces of information from parent
subsystems. Moreover, differently from methods based on moving horizon
estimation, our approach does not require the on-line solution to optimization
problems. Our state-estimation scheme, that is based on the notion of practical
robust positive invariance developed in Rakovic 2011, also guarantees
satisfaction of constraints on local estimation errors and it can be updated
with a limited computational effort when subsystems are added or removed
A novel distributed algorithm for estimation and control of large-scale systems
In this paper we propose a novel algorithm based on linear matrix inequalities for the design of distributed controllers and state estimators for large-scale systems inspired by linear quadratic regulators and Kalman filters, respectively. With respect to similar state-of-the art methods, the scheme proposed here allows to reduce the conservativeness due to the approximations used for the covariance distributed iterative computation. The theoretical properties of the proposed scheme are thoroughly investigated.
The controllers and observers obtained using the proposed approach are applied to a simulated dynamical system and their performances are thoroughly compared to those obtained with state-of-the-art schemes, showing the potentialities of the scheme
The scope of the Kalman filter for spatio-temporal applications in environmental science
The Kalman filter is a workhorse of dynamical modeling. But there are challenges when using the Kalman filter in environmental science: the complexity of environmental processes, the complicated and irregular nature of many environmental datasets, and the scale of environmental datasets, which may comprise many thousands of observations per time-step. We show how these challenges can be met within the Kalman filter, identifying some situations which are relatively easy to handle, such as datasets which are high-resolution in time, and some which are hard, like areal observations on small contiguous polygons. Overall, we conclude that many applications in environmental science are within the scope of the Kalman filter, or its generalizations
An iterated block particle filter for inference on coupled dynamic systems with shared and unit-specific parameters
We consider inference for a collection of partially observed, stochastic,
interacting, nonlinear dynamic processes. Each process is identified with a
label called its unit, and our primary motivation arises in biological
metapopulation systems where a unit corresponds to a spatially distinct
sub-population. Metapopulation systems are characterized by strong dependence
through time within a single unit and relatively weak interactions between
units, and these properties make block particle filters an effective tool for
simulation-based likelihood evaluation. Iterated filtering algorithms can
facilitate likelihood maximization for simulation-based filters. We introduce
an iterated block particle filter applicable when parameters are unit-specific
or shared between units. We demonstrate this algorithm by performing inference
on a coupled epidemiological model describing spatiotemporal measles case
report data for twenty towns
Design study of a low cost civil aviation GPS receiver system
A low cost Navstar receiver system for civil aviation applications was defined. User objectives and constraints were established. Alternative navigation processing design trades were evaluated. Receiver hardware was synthesized by comparing technology projections with various candidate system designs. A control display unit design was recommended as the result of field test experience with Phase I GPS sets and a review of special human factors for general aviation users. Areas requiring technology development to ensure a low cost Navstar Set in the 1985 timeframe were identified
Modeling temporal networks with dynamic stochastic block models
Osservando il recente interesse per le reti dinamiche temporali e l'ampio numero di campi di applicazione, questa tesi ha due principali propositi: primo, di analizzare alcuni modelli teorici di reti temporali, specialmente lo stochastic blockmodel dinamico, al fine di descrivere la dinamica di sistemi reali e fare previsioni. Il secondo proposito della tesi è quello di creare due nuovi modelli teorici, basati sulla teoria dei processi autoregressivi, dai quali inferire nuovi parametri dalle reti temporali, come la matrice di evoluzione di stato e una migliore stima della varianza del rumore del processo di evoluzione temporale. Infine, tutti i modelli sono testati su un data set interbancario: questi rivelano la presenza di un evento atteso che divide la rete temporale in due periodi distinti con differenti configurazioni e parametri
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