Name
Abstract | ||
|---|---|---|
In this paper, a new filtering method for hybrid Markovian switching systems is presented. The method is called the multiple model multiple hypothesis filter (M^3H filter). For each hypothesis an (extended) Kalman filter is running. An hypothesis represents a specific model mode sequence history. The proposed method is highly adaptive and flexible. The main feature is that the number of hypotheses that are maintained varies with the 'difficulty' of the situation and that it is adaptive in its computational load. In a representative example it is shown that the M^3H filter can outperform the widely used interacting multiple model (IMM) filter, both in terms of accuracy and computational load. The newly proposed filter is an excellent alternative for the widely used and celebrated IMM filter. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.automatica.2004.11.018 | Automatica |
Keywords | Field | DocType |
Hybrid systems,Adaptive filtering,IMM,Kalman filters,Target tracking | Alpha beta filter,Extended Kalman filter,Digital filter,Root-raised-cosine filter,Control theory,Filtering problem,Kernel adaptive filter,Adaptive filter,Mathematics,Filter design | Journal |
Volume | Issue | ISSN |
41 | 4 | Automatica |
Citations | PageRank | References |
11 | 0.96 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. Boers | 1 | 135 | 18.13 |
| Hans Driessen | 2 | 59 | 7.31 |