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 …

This paper presents a simple yet efficient adaptive Kalman filter for tracking targets expected
to perform varying turn maneuvers. The process noise covariance level of a second order …

KALMAN filter (KF) is an algorithm for op-timal (minimum variance) estimation of the state
vector of a linear, dynamic system driven by stochastic non-stationary inputs, based on …

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 unscented transformation coupled with certain parts of the classic Kalman Alter,
provides a more accurate method than the Extended Kalman Filter for nonlinear state …

Kalman filter tuning is based on the process and measurement noise covariances that are
often obtained by ad hoc methods. After the filter is tuned, it is necessary to evaluate the …

The Kalman filter is the Bayesian optimum solution to the problem of sequentially estimating
the states of a dynamical system in which the state evolution and measurement processes …

It is well known that the extended Kalman filtering methodology works well in situations
characterized by a high signal-to-noise ratio, good observability and a valid state trajectory …

The performance of a linear Kalman filter will degrade when the dynamic noise is not
Gaussian. A robust Kalman filter based on the m-interval polynomial approximation (MIPA) …

The normal unscented Kalman filter (UKF) suffers from performance degradation and even
divergence while mismatch between the noise distribution assumed to be known as a priori …