| bibtype |
J -
Journal Article
|
| ARLID |
0532181 |
| utime |
20250310141540.7 |
| mtime |
20200916235959.9 |
| SCOPUS |
85092572093 |
| WOS |
000574739900010 |
| DOI |
10.1109/TSP.2020.3023823 |
| title
(primary) (eng) |
Collaborative sequential state estimation under unknown heterogeneous noise covariance matrices |
| specification |
| page_count |
14 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0256727 |
| ISSN |
1053-587X |
| title
|
IEEE Transactions on Signal Processing |
| volume_id |
68 |
| volume |
10 (2020) |
| page_num |
5365-5378 |
|
| keyword |
Diffusion network |
| keyword |
Diffusion strategz |
| keyword |
State estimation |
| author
(primary) |
| ARLID |
cav_un_auth*0242543 |
| name1 |
Dedecius |
| name2 |
Kamil |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| full_dept |
Department of Adaptive Systems |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0267768 |
| name1 |
Tichý |
| name2 |
Ondřej |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA20-27939S |
| ARLID |
cav_un_auth*0391986 |
| agency |
GA ČR |
|
| abstract
(eng) |
We study the problem of distributed sequential estimation of common states and measurement noise covariance matrices of hidden Markov models by networks of collaborating nodes. We adopt a realistic assumption that the true covariance matrices are possibly different (heterogeneous) across the network. This setting is frequent in many distributed real-world systems where the sensors (e.g., radars) are deployed in a spatially anisotropic environment, or where the networks may consist of sensors with different measuring principles (e.g., using different wavelengths). Our solution is rooted in the variational Bayesian paradigm. In order to improve the estimation performance, the measurements and the posterior estimates are communicated among adjacent neighbors within one network hop distance using the information diffusion strategy. The resulting adaptive algorithm selects neighbors with compatible information to prevent degradation of estimates. |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20202 |
| reportyear |
2021 |
| num_of_auth |
2 |
| mrcbC52 |
2 R hod 4 4rh 4 20250310141527.1 4 20250310141540.7 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0310804 |
| confidential |
S |
| mrcbC86 |
3+4 Article Engineering Electrical Electronic |
| mrcbC91 |
C |
| mrcbT16-e |
ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-i |
8.68988 |
| mrcbT16-j |
1.701 |
| mrcbT16-s |
1.638 |
| mrcbT16-B |
91.464 |
| mrcbT16-D |
Q1* |
| mrcbT16-E |
Q1* |
| arlyear |
2020 |
| mrcbTft |
\nSoubory v repozitáři: dedecius-532181.pdf |
| mrcbU14 |
85092572093 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000574739900010 WOS |
| mrcbU63 |
cav_un_epca*0256727 IEEE Transactions on Signal Processing 1053-587X 1941-0476 Roč. 68 č. 10 2020 5365 5378 |
|