The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for
nonlinear systems. However, more than 35 years of experience in the estimation community …

Since the landmark work of RE Kalman in the 1960s, considerable efforts have been
devoted to time series state space models for a large variety of dynamic estimation …

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 …

This paper describes a new approach for generalizing the Kalman filter to nonlinear
systems. A set of samples are used to parametrize the mean and covariance of a (not …

For most tracking applications the Kalman filter is reliable and efficient, but it is limited to a
relatively restricted class of linear Gaussian problems. To solve problems beyond this …

This paper describes a generalisation of the unscented transformation (UT) which allows
sigma points to be scaled to an arbitrary dimension. The UT is a method for predicting …

The emerging technology of multisensor data fusion has a wide range of applications, both
in Department of Defense (DoD) areas and in the civilian arena. The techniques of …

The Unscented Transform (UT) approximates the result of applying a specified nonlinear
transformation to a given mean and covariance estimate. The UT works by constructing a set …

The exact compensation for the bias in the classical polar-to-Cartesian conversion is shown
to be multiplicative and to depend on the statistics of the cosine of the angle measurement …

This paper describes a new and better-behaved sigma point selection strategy for the
unscented transformation (UT). The UT approximates the result of applying a specified …