Kalman filtering, with focus on limiting the required communication bandwidth. By distributed
we refer to a scenario when all nodes in the network desire an estimate of the full state of the …

In this correspondence paper, we provide a new look at the boundedness problems of error
covariance of Kalman filtering. First, by utilizing the mathematical induction technique, a new …

The Kalman Filter (KF) is one of the most widely used methods for tracking and estimation
due to its simplicity, optimality, tractability and robustness. However, the application of the KF …

The paper focuses on the state estimation problem of nonlinear non-Gaussian systems with
state subject to a nonlinear inequality constraint. Taking into account the available additional …

In most solutions to state estimation problems, eg, target tracking, it is generally assumed
that the state transition and measurement models are known a priori. However, there are …

In this paper, an optimization-based adaptive Kalman filtering method is proposed. The
method produces an estimate of the process noise covariance matrix Q by solving an …

Redundant attitude representations are often used in Kalman filters for estimating dynamic
states that include an attitude. A minimal three-element attitude deviation is combined with a …