0532181 20250310141540.720200916235959.9 85092572093 000574739900010 10.1109/TSP.2020.3023823 14 s. P cav_un_epca*02567271053-587X6810 (2020)5365-5378 Diffusion network Diffusion strategz State estimation cav_un_auth*0242543 Dedecius Kamil UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0267768 Tichý Ondřej UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2020/AS/dedecius-0532181.pdf https://ieeexplore.ieee.org/document/9195780 GA20-27939S cav_un_auth*0391986 GA ČR 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. WOS BB 20000 20200 20202 2021 2 2 R hod 4 4rh 4 20250310141527.1 4 20250310141540.7 RVO:67985556 http://hdl.handle.net/11104/0310804 S 3+4 Article Engineering Electrical Electronic C ENGINEERING.ELECTRICAL&ELECTRONIC 5.239 0.767 9.1 0.04146 1.701 39639 314 1.638 45.4 6.24 9132 Q1 4.333 468 Q1 91.464 85.2 Q1* Q1* 1.6 Q1 85.165 2020 Soubory v repozitáři: dedecius-532181.pdf 85092572093 SCOPUS PUBMED 000574739900010 WOS cav_un_epca*0256727 IEEE Transactions on Signal Processing 1053-587X 1941-0476 Roč. 68 č. 10 2020 5365 5378