Name
Abstract | ||
|---|---|---|
This paper is concerned with distributed fusion (DF) estimation problem for nonlinear multi-sensor systems with correlated noises. Based on a recursive linear minimum variance estimation (RLMVE) framework, a novel filter is developed. It is proved that the RLMVE-based filter and the existing de-correlated filter have the functional equivalence. Then, for multi-sensor cases, cross-covariance matrices between any two local filters are derived. Based on the RLMVE-based filter and cross-covariance matrices, a DF filter weighted by matrices is proposed in the sense of linear minimum variance. Finally, based on the existing de-correlated filter, the algorithm of cross-covariance for de-correlated systems and the DF algorithm weighted by matrices, a de-correlated DF filtering algorithm is proposed. An example verifies the effectiveness of the proposed RLMVE-based DF filter. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2020.2976201 | IEEE ACCESS |
Keywords | DocType | Volume |
Estimation,Nonlinear systems,Noise measurement,Mathematical model,Kalman filters,Covariance matrices,Gaussian noise,Multi-sensor,nonlinear system,distributed fusion filter,cross-covariance matrix,linear minimum variance estimation | Journal | 8 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 2 | |