This paper provides an overview of nonlinear state estimation techniques along with a
discussion on the challenges and opportunities for future work in the field. Emphasis is given …

The definitive textbook and professional reference on Kalman Filtering–fully updated,
revised, and expanded This book contains the latest developments in the implementation …

A Kalman filter requires an exact knowledge of the process noise covariance matrix Q and
the measurement noise covariance matrix R. Here we consider the case in which the true …

Statistical Orbit Determination presents fundmentals of orbit determination--from weighted
least squares approaches (Gauss) to today's high-speed computer algorithms that provide …

The different methods of adaptive filtering are divided into four categories: Bayesian,
maximum likelihood (ML), correlation, and covariance matching. The relationship between …

The topics of control engineering and signal processing continue to flourish and develop. In
common with general scientific investigation, new ideas, concepts and interpretations …

The Kalman filter requires knowledge of the noise statistics; however, the noise covariances
are generally unknown. Although this problem has a long history, reliable algorithms for their …

We present an adaptive smoother for linear state-space models with unknown process and
measurement noise covariances. The proposed method utilizes the variational Bayes …

The Kalman filter gain can be extracted from output signals but the covariance of the state
error cannot be evaluated without knowledge of the covariance of the process and …

The extended Kalman filter (EKF) is a widely adopted method for sensor fusion in navigation
applications. A crucial aspect of the EKF is the online determination of the process noise …