Name
Abstract | ||
|---|---|---|
A formulation of the hypothesised filter for independent stochastic populations (HISP) is proposed, based on the concept. of association measure, which is a measure on the set of observation histories. Using this formulation, a particle approximation is introduced at the level of the association measure for handling the exponential growth in the number of underlying hypotheses. This approximation is combined with a sequential Monte Carlo implementation for the underlying single-object distributions to form a mixed particle association model. Finally, the performance of this approach is compared against a Kalman filter implementation on simulated data based on a finite-resolution sensor. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/EUSIPCO.2015.7362584 | European Signal Processing Conference |
Keywords | Field | DocType |
Multi-object filtering,finite-resolution sensor | Applied mathematics,Mathematical optimization,Extended Kalman filter,Particle filter,Kalman filter,Atmospheric measurements,Ensemble Kalman filter,Mathematics,Exponential growth | Conference |
ISSN | Citations | PageRank |
2076-1465 | 0 | 0.34 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeremie Houssineau | 1 | 34 | 9.57 |
| Daniel E. Clark | 2 | 360 | 36.76 |
| Pierre Del Moral | 3 | 139 | 18.60 |