Nonlinear non-Gaussian state-space models arise in numerous applications in control and
signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters …

This paper is concerned with the parameter estimation of a general class of nonlinear
dynamic systems in state-space form. More specifically, a Maximum Likelihood (ML) …

As recent advances in calcium sensing technologies facilitate simultaneously imaging action
potentials in neuronal populations, complementary analytical tools must also be developed …

In this paper we present a statistical approach to estimating the time in which an operating
gear will reach a critical stage. The approach relies on measured vibration signals. From …

NONLINEAR FILTERS Discover the utility of using deep learning and (deep) reinforcement
learning in deriving filtering algorithms with this insightful and powerful new resource …

This article addresses the problem of tracking an elliptical object (eg, a vehicle or aircraft
carrier) with unknown but fixed lengths of axes. In practice, such axis lengths are usually …

This article proposes a maximum likelihood algorithm for simultaneous estimation of state
and parameter values in nonlinear stochastic state-space models. The proposed algorithm …

In this paper, we consider structure detection and parameter estimation of the nonlinear auto-
regressive with exogenous inputs (NARX) model, using the EM (expectation–maximisation) …

This paper investigates a parameter estimation problem for batch processes through the
maximum likelihood method. In batch processes, the initial state usually relates to the states …

Nonlinear Filtering covers linear and nonlinear filtering in a comprehensive manner, with
appropriate theoretic and practical development. Aspects of modeling, estimation, recursive …