Kalman filters have long stood as a cornerstone in the field of target tracking and state estimation, providing an optimally recursive solution for estimating the state of dynamic systems in the ...

Therefore, the filter like the Maximum Correntropy Kalman Filter (MCKF) could not achieve the good performance under some complex non-Gaussian noise.

Applies this understanding to enhancing the robustness of the filter and to extend to applications including prediction and smoothing. Shows how to implement a target-tracking application in Octave ...

Numerical basics -- Method of least squares -- Recursive least-quares filtering -- Polynomial Kalman filters -- Kalman filters in a nonpolynomial world -- Continuous polynomial Kalman filter -- ...

The sensor fusion is taken care of with a Kalman filter, smoothed with the typical Rauch-Tung-Striebel (RTS) algorithm before being passed onto the final application.

The sensor fusion is taken care of with a Kalman filter, smoothed with the typical Rauch-Tung-Striebel (RTS) algorithm before being passed onto the final application.

The use of Kalman filter methods for high-performance order tracking of noise and vibration signals was introduced in 1993. Based on experience with that original formulation, further work has ...

Kalman filters have long stood as a cornerstone in the field of target tracking and state estimation, providing an optimally recursive solution for estimating the state of dynamic systems in the ...