Name
Abstract | ||
|---|---|---|
This paper presents an approach to accurate and scalable multiple-model state estimation for hybrid systems with intermittent, multi-modal dynamics. The approach consists of using discrete-state estimation to identify a system's behavioral context and determine which motion models appropriately represent current dynamics, and which multiple-model filters are appropriate for state estimation. This improves the accuracy and scalability of conventional multiple-model state estimation. This approach is validated experimentally on a mobile robot that exhibits multi-modal dynamics. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IROS.2007.4399110 | IROS |
Keywords | Field | DocType |
multiple-model state estimation,multiple-model filters,hybrid systems,finite automata,index terms— hidden markov models,timed automata,state estimation,intermittent multimodal dynamics,multiple-model filtering,hidden markov models,discrete-state estimation,filtering theory,context identification,continuous systems,discrete systems,hybrid system,mobile robot,hidden markov model,indexing terms | Computer science,Finite-state machine,Control engineering,Theoretical computer science,Hidden Markov model,Filtering theory,Hybrid system,Computer engineering,Mobile robot,Scalability | Conference |
ISBN | Citations | PageRank |
978-1-4244-0912-9 | 3 | 0.43 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarjoun Skaff | 1 | 40 | 4.36 |
| Alfred A. Rizzi | 2 | 1208 | 179.03 |
| Howie Choset | 3 | 2826 | 257.12 |