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 ...

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 ...

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.

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

Vold and Leuridan [1] introduced in 1993 an algorithm for high resolution, slew rate independent order tracking based on the concepts of Kalman filters [5, 6]. The algorithm has been highly successful ...