Kalman Filtering and Clustering in Sensor Networks

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

Organisaatiot

  • Norwegian University of Science and Technology

Kuvaus

In this work, a distributed Kalman filtering and clustering framework for sensor networks tasked with tracking multiple state vector sequences is developed. This is achieved through recursively updating the likelihood of a state vector estimation from one agent offering valid information about the state vector of its neighbors, given the available observation data. These likelihoods then form the diffusion coefficients, used for information fusion over the sensor network. For rigour, the mean and mean square behavior of the developed Kalman filtering and clustering framework is analyzed, convergence criteria are established, and the performance of the developed framework is demonstrated in a simulation example.

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
TilaJulkaistu - 10 syyskuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Conference on Acoustics, Speech, and Signal Processing - Calgary, Kanada
Kesto: 15 huhtikuuta 201820 huhtikuuta 2018
https://2018.ieeeicassp.org/

Julkaisusarja

NimiProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN (elektroninen)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
LyhennettäICASSP
MaaKanada
KaupunkiCalgary
Ajanjakso15/04/201820/04/2018
www-osoite

ID: 28749085