Name
Abstract | ||
|---|---|---|
This paper presents a robust, non-linear smoothing algorithm and develops the theory behind it. This algorithm is extremely robust to outliers and missing data and handles state-dependent noise. Implementing it is straightforward as it consists mainly of two sub-routines: (a) the Rauch-Tung-Striebel recursions, or Kalman smoother; and (b) a backtracking line search strategy. The computational load grows linearly with the number of data because the algorithm preserves the underlying structure of the problem. Global convergence to a local optimum is guaranteed, under mild assumptions. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/IVS.2013.6629464 | Intelligent Vehicles Symposium |
Keywords | Field | DocType |
convergence,road vehicles,search problems,smoothing methods,state estimation,Kalman smoother,Rauch-Tung-Striebel recursions,backtracking line search strategy,global convergence,robust nonlinear smoothing algorithm,state-dependent noise,vehicle state estimation | Convergence (routing),Kalman smoother,Mathematical optimization,Nonlinear system,Local optimum,Outlier,Backtracking line search,Smoothing,Missing data,Mathematics | Conference |
ISSN | ISBN | Citations |
1931-0587 | 978-1-4673-2754-1 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel Agamennoni | 1 | 194 | 16.42 |
| Stewart Worrall | 2 | 157 | 23.78 |
| James R. Ward | 3 | 18 | 4.76 |
| Eduardo Mario Nebot | 4 | 1255 | 224.24 |