333 research outputs found
Non-Asymptotic Kernel-based Parametric Estimation of Continuous-time Linear Systems
In this paper, a novel framework to address the problem of parametric estimation for continuous-time linear time-invariant dynamic systems is dealt with. The proposed methodology entails the design of suitable kernels of non-anticipative linear integral operators thus obtaining estimators showing, in the ideal case, \u201cnon-asymptotic\u201d (i.e., \u201cfinite-time\u201d) convergence. The analysis of the properties of the kernels guaranteeing such a convergence
behaviour is addressed and a novel class of admissible kernel functions is introduced. The operators induced by the proposed kernels admit implementable (i.e., finite-dimensional and internally stable) state-space realizations. Extensive numerical results are reported to show the effectiveness of the proposed methodology. Comparisons with some existing continuous-time estimators are addressed as well and insights on the possible bias affecting the estimates are provided
Numerical Fitting-based Likelihood Calculation to Speed up the Particle Filter
The likelihood calculation of a vast number of particles is the computational
bottleneck for the particle filter in applications where the observation
information is rich. For fast computing the likelihood of particles, a
numerical fitting approach is proposed to construct the Likelihood Probability
Density Function (Li-PDF) by using a comparably small number of so-called
fulcrums. The likelihood of particles is thereby analytically inferred,
explicitly or implicitly, based on the Li-PDF instead of directly computed by
utilizing the observation, which can significantly reduce the computation and
enables real time filtering. The proposed approach guarantees the estimation
quality when an appropriate fitting function and properly distributed fulcrums
are used. The details for construction of the fitting function and fulcrums are
addressed respectively in detail. In particular, to deal with multivariate
fitting, the nonparametric kernel density estimator is presented which is
flexible and convenient for implicit Li-PDF implementation. Simulation
comparison with a variety of existing approaches on a benchmark 1-dimensional
model and multi-dimensional robot localization and visual tracking demonstrate
the validity of our approach.Comment: 42 pages, 17 figures, 4 tables and 1 appendix. This paper is a
draft/preprint of one paper submitted to the IEEE Transaction
Optimal insider control of stochastic partial differential equations
We study the problem of optimal inside control of an SPDE (a stochastic
evolution equation) driven by a Brownian motion and a Poisson random measure.
Our optimal control problem is new in two ways: (i) The controller has access
to inside information, i.e. access to information about a future state of the
system, (ii) The integro-differential operator of the SPDE might depend on the
control.
In the first part of the paper, we formulate a sufficient and a necessary
maximum principle for this type of control problem, in two cases: (1) When the
control is allowed to depend both on time t and on the space variable x. (2)
When the control is not allowed to depend on x.
In the second part of the paper, we apply the results above to the problem of
optimal control of an SDE system when the inside controller has only noisy
observations of the state of the system. Using results from nonlinear
filtering, we transform this noisy observation SDE inside control problem into
a full observation SPDE insider control problem.
The results are illustrated by explicit examples
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