Kalman Filtering in Wireless Sensor Networks: Reducing communication cost in state-estimation problems

Alejandro Ribeiro, Ioannis D. Schizas, Stergios I. Roumeliotis, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

107 Scopus citations
Original languageEnglish (US)
Pages (from-to)66-86
Number of pages21
JournalIEEE Control Systems
Issue number2
StatePublished - Apr 2010

Bibliographical note

Funding Information:
Ioannis D. Schizas received the diploma in computer engineering and informatics (with honors) from the University of Patras, Greece, in 2004 and the M.Sc. in electrical and computer engineering from the University of Minnesota, Minneapolis, in 2007. Since August 2004, he has been working toward the Ph.D. in the Department of Electrical and Computer Engineering, the University of Minnesota, Minneapolis. His research interests lie in the areas of communication theory, signal processing, and networking. His current research focuses on distributed signal processing with wireless sensor networks as well as distributed compression and source coding. Stergios I. Roumeliotis received the diploma in electrical engineering from the National Technical University of Athens, Greece, in 1995 and the M.S. and Ph.D. in electrical engineering from the University of Southern California in 1999 and 2000, respectively. From 2000 to 2002 he was a postdoctoral fellow at the California Institute of Technology. Since 2002 he has been with the Department of Computer Science and Engineering at the University of Minnesota, where he is currently an associate professor. He is the recipient of the NSF PECASE award, the McKnight Land-Grant Professorship award, and a NASA Tech Briefs award. His research interests include inertial navigation of aerial and ground autonomous vehicles, fault detection and identification, and sensor networks. Recently, his research has focused on distributed estimation under communication and processing constraints as well as active sensing for reconfigurable networks of mobile sensors.

Funding Information:
This research was supported in part by NSF Award 0931239, USDoD ARO Grant W911NF-05-1-0283, and also through collaborative participation in the C&N Consortium sponsored by the U.S. ARL under the CTA Program, Cooperative Agreement DAAD19-01-2-0011. The authors would like to thank Daniel Stilwell and Darren Maczka at Virginia Tech for providing the autonomous underwater vehicle experimental data; Esha Nerurkar and Nikolas Trawny at the University of Minnesota for their help with the iterative extended SOI-KF experimental validation; and Hao Zhu for providing the scripts to generate Figure 16.

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