Name
Abstract | ||
|---|---|---|
We consider the problem of robust estimation involving filtering and smoothing for nonlinear state space models which are disturbed by heavy-tailed impulsive noises. To deal with heavy-tailed noises and improve the robustness of the traditional nonlinear Gaussian Kalman filter and smoother, we propose in this work a general framework of robust filtering and smoothing, which adopts a new maximum correntropy criterion to replace the minimum mean square error for state estimation. To facilitate understanding, we present our robust framework in conjunction with the cubature Kalman filter and smoother. A half-quadratic optimization method is utilized to solve the formulated robust estimation problems, which leads to a new maximum correntropy derivative-free robust Kalman filter and smoother. Simulation results show that the proposed methods achieve a substantial performance improvement over the conventional and existing robust ones with slight computational time increase. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2880618 | IEEE ACCESS |
Keywords | Field | DocType |
Robust Kalman filtering,robust Kalman smoothing,maximum correntropy criterion,heavy-tailed noise,half-quadratic minimization | Nonlinear system,Computer science,Minimum mean square error,Algorithm,Filter (signal processing),Kalman filter,Robustness (computer science),Smoothing,Gaussian,State space,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongwei Wang | 1 | 11 | 8.68 |
| Hongbin Li | 2 | 137 | 11.40 |
| Wei Zhang | 3 | 0 | 1.35 |
| Junyi Zuo | 4 | 0 | 1.35 |
| Heping Wang | 5 | 11 | 5.50 |