Prediction algorithms enable computers to learn from historical data in order to make
accurate decisions about an uncertain future to maximize expected benefit or avoid potential …

The Kalman filter (KF) is used extensively for state estimation. Among its requirements are
the process and observation noise covariances, which are unknown or partially known in …

Traditional adaptive Kalman filtering algorithms based on innovation are often used to solve
the problem of reduced or even divergent filtering estimation accuracy under abnormal …

To effectively maintain and analyze a large amount of real-time sensor data, one often uses
a filtering technique that reflects characteristics of original data well. This paper proposes a …

Adaptive and robust methods are two opposite strategies to be adopted in the Kalman filter
when the difference between the predictive observation and the actual observation, ie the …

The performance of the Kalman filter (KF), which is recognized as an outstanding tool for
dynamic system state estimation, heavily depends on its parameter R, called the …

Traditionally, Kalman filter (KF) is designed with the assumptions of non-delayed
measurements and additive white Gaussian noises. However, practical problems often fail to …

Kalman filter is a well known adaptive filtering algorithm, widely used for target tracking
applications. When the system model and measurements are non linear, variation of Kalman …

Noise distribution plays an essential role in state estimation using Kalman filter. However,
statistical characteristics of the noise are often unknown in most practical applications. A …

The Kalman filter requires knowledge about the noise statistics. In practical applications,
however, the noise covariances are generally not known. In this paper, a method for …