14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper FrB2.4

Huang, Minyi (Univ. of Melbourne), Dey, Subhrakanti (Univ. of Melbourne)

Dynamic Quantization for Multi-Sensor Estimation Over Bandlimited Fading Channels with Fusion Center Feedback

Scheduled for presentation during the Regular Session "Filtering and Smoothing" (FrB2), Friday, March 31, 2006, 16:30−16:50, Banquet Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 16, 2018

Keywords Filtering and Smoothing, Time Series, Blind Estimation


This paper considers the state estimation of hidden Markov models (HMMs) in a network of sensors which communicate with the fusion center via finite symbols by fading channels. The objective is to minimize the long term mean square estimation error for the underlying Markov chain. By using feedback from the fusion center, a dynamic quantization scheme for the sensor nodes is proposed and analyzed by a Markov decision approach. The performance improvement by feedback, as well as the effect of fading, is illustrated.