State estimation under non-Gaussian Levy noise: A modified Kalman filtering method
This addresses a problem in signal processing and control systems where noise is non-Gaussian, but it appears incremental as it modifies an existing method for a specific noise type.
The paper tackled state estimation for linear systems under non-Gaussian Lévy noise, which can have infinite variance and cause conventional Kalman filters to fail, by devising a modified Kalman filter that works effectively with reasonable computational cost, as demonstrated through simulation results.
The Kalman filter is extensively used for state estimation for linear systems under Gaussian noise. When non-Gaussian Lévy noise is present, the conventional Kalman filter may fail to be effective due to the fact that the non-Gaussian Lévy noise may have infinite variance. A modified Kalman filter for linear systems with non-Gaussian Lévy noise is devised. It works effectively with reasonable computational cost. Simulation results are presented to illustrate this non-Gaussian filtering method.