Distributed Kalman filtering based on quantized innovations

Eric J. Msechu, Alejandro Ribeiro, Stergios I. Roumeliotis, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

We consider state estimation of a Markov stochastic process using an ad hoc wireless sensor network (WSN) based on noisy linear observations. Due to power and bandwidth constraints present in resource-limited WSNs, the observations are quantized before transmission. We derive a distributed recursive mean-square error (MSE) optimal quantizer-estimator based on the quantized observations. The resultant Kalman-like algorithm based on quantized observations exhibits MSE performance and computational complexity comparable to the Kalman filter based on un-quantized observations even for 2-3 bits of quantization per observation.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages3293-3296
Number of pages4
DOIs
StatePublished - Sep 16 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: Mar 31 2008Apr 4 2008

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
CountryUnited States
CityLas Vegas, NV
Period3/31/084/4/08

Keywords

  • Distributed state estimation
  • Kalman filtering
  • Limited-rate communication
  • Target tracking
  • Wireless sensor networks

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