Distributed Kalman filtering based on quantized innovations

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

doi:10.1109/ICASSP.2008.4518354

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 proceeding › Conference 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 language	English (US)
Title of host publication	2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages	3293-3296
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2008.4518354
State	Published - 2008
Event	2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States Duration: Mar 31 2008 → Apr 4 2008

Publication series

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

Other

Other	2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Country/Territory	United States
City	Las Vegas, NV
Period	3/31/08 → 4/4/08

Keywords

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

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10.1109/ICASSP.2008.4518354

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Msechu, E. J., Ribeiro, A., Roumeliotis, S. I., & Giannakis, G. B. (2008). Distributed Kalman filtering based on quantized innovations. In 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP (pp. 3293-3296). Article 4518354 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2008.4518354

Distributed Kalman filtering based on quantized innovations. / Msechu, Eric J.; Ribeiro, Alejandro; Roumeliotis, Stergios I. et al.
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP. 2008. p. 3293-3296 4518354 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Msechu, EJ, Ribeiro, A, Roumeliotis, SI & Giannakis, GB 2008, Distributed Kalman filtering based on quantized innovations. in 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP., 4518354, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 3293-3296, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, Las Vegas, NV, United States, 3/31/08. https://doi.org/10.1109/ICASSP.2008.4518354

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title = "Distributed Kalman filtering based on quantized innovations",

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.",

keywords = "Distributed state estimation, Kalman filtering, Limited-rate communication, Target tracking, Wireless sensor networks",

author = "Msechu, {Eric J.} and Alejandro Ribeiro and Roumeliotis, {Stergios I.} and Giannakis, {Georgios B.}",

year = "2008",

doi = "10.1109/ICASSP.2008.4518354",

language = "English (US)",

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AU - Ribeiro, Alejandro

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N2 - 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.

AB - 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.

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KW - Kalman filtering

KW - Limited-rate communication

KW - Target tracking

KW - Wireless sensor networks

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ER -

Distributed Kalman filtering based on quantized innovations

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