The Kalman filter is the minimum-variance state estimator for linear dynamic systems with
Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear …

This paper deals with the state estimation problem for linear systems with linear state
equality constraints. Using noisy measurements which are available from the observable …

Due to its potential applications in robotics and navigation, recent years have witnessed
some progress in Kalman filter (KF) with norm constraints on the state. A noticeable …

This technical communique considers a practical scenario where a classical estimation
method might have already been implemented on a certain platform when one tries to apply …

Existing works in optimal filtering for linear Gaussian systems with arbitrary unknown inputs
assume perfect knowledge of the noise covariances in the filter design. This is impractical …

We present an algorithm for a class of statistical inference problems. The main idea is to
reformulate the inference problem as an optimization procedure, based on the generation of …

The ability to perform online model identification for nonlinear systems with unknown
dynamics is essential to any adaptive model-based control system. In this paper, a new …

This book describes how prior knowledge about dynamical systems and functions can be
integrated in mathematical modelling. The first part comprises the transformation of the …

This paper addresses ground target tracking (GTT) for airborne radar. Digital terrain
elevation data (DTED) are widely used for GTT as prior information under the premise that …

This paper deals with the state estimation problem for linear systems with state equality
constraints. Using noisy measurements which are available from the observable system, we …