Name
Abstract | ||
|---|---|---|
Risk-sensitive filters (RSF) put a penalty to higher-order moments of the estimation error compared to conventional filters as the Kalman filter minimizing the mean square error. The result is a more cautious filter, which can be interpreted as an implicit and automatic way to increase the state noise covariance. On the other hand, the process of jittering, or roughening, is well-known in particle filters to mitigate sample impoverishment. The purpose of this contribution is to introduce risk-sensitive particle filters (RSPF) as an alternative approach to mitigate sample impoverishment based on constructing explicit risk functions from a general class of factorizable functions. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TSP.2008.928520 | IEEE Transactions on Signal Processing - Part II |
Keywords | Field | DocType |
risk-sensitive particle filters,cautious filter,kalman filter,general risk function,estimation error,risk-sensitive particle filter,index terms— risk sensitive,sample impoverishment,conventional filter,alternative approach,particle filter,explicit risk function,risk sensitive particle filters,mean square error,various risk function,risk function,risk-sensitive filter,mitigating sample impoverishment,control theory,higher order,cost function,control engineering,particle filters,signal processing,monte carlo methods,kernel | Kernel (linear algebra),Signal processing,Monte Carlo method,Mathematical optimization,Particle filter,Algorithm,Mean squared error,Kalman filter,Mathematics,Covariance | Journal |
Volume | Issue | ISSN |
56 | 10 | 1053-587X |
Citations | PageRank | References |
3 | 0.62 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Umut Orguner | 1 | 548 | 40.11 |
| Fredrik Gustafsson | 2 | 2287 | 281.33 |