| project |
| ARLID |
cav_un_auth*0331019 |
| project_id |
GA16-09848S |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
The marginalized particle filtering (MPF) is a powerful technique reducing the number of particles necessary to effectively estimate hidden states of state-space models. This paper alleviates the assumption of a fully known and computationally tractable observation model. Exploiting the recent developments in the theory of approximate Bayesian computation (ABC) filtration, an ABC counterpart of MPF is proposed, applicable when the observation model is too complex to be evaluated analytically or even numerically, but it is still possible to sample from it by plugging in the state. The novelty is 2-fold. First, ABC methods have not been used in marginalized filtering yet. Second, a new multivariate robust method for evaluation of particle weights is proposed. The goal of this paper is to demonstrate the idea on the background of the MPF with a particular accent on exposition. |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2019 |
| num_of_auth |
1 |
| mrcbC52 |
4 A hod 4ah 20231122142640.9 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0274422 |
| mrcbC64 |
1 Department of Adaptive Systems UTIA-B 10200 COMPUTER SCIENCE, CYBERNETICS |
| confidential |
S |
| mrcbC86 |
3+4 Article Automation Control Systems|Engineering Electrical Electronic |
| mrcbT16-e |
AUTOMATIONCONTROLSYSTEMS|ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-j |
0.655 |
| mrcbT16-s |
0.885 |
| mrcbT16-B |
48.708 |
| mrcbT16-D |
Q3 |
| mrcbT16-E |
Q2 |
| arlyear |
2018 |
| mrcbTft |
\nSoubory v repozitáři: dedecius-0478074.pdf |
| mrcbU14 |
85030092933 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000419919900001 WOS |
| mrcbU63 |
cav_un_epca*0256772 International Journal of Adaptive Control and Signal Processing 0890-6327 1099-1115 Roč. 32 č. 1 2018 1 12 Wiley |