Data-driven online variational filtering inwireless sensor networks

Hichem Snoussi; Jean Yves Tourneret; Petar M. Djurić; Cédric Richard

doi:10.1109/ICASSP.2009.4960108

Data-driven online variational filtering inwireless sensor networks

Hichem Snoussi
, Jean Yves Tourneret
, Petar M. Djurić
, Cédric Richard

Electrical Engineering

Université de technologie de Troyes
Université Fédérale Toulouse Midi-Pyrénées

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

Abstract

In this paper, a data-driven extension of the variational algorithm is proposed. Based on a few selected sensors, target tracking is performed distributively without any information about the observation model. Tracking under such conditions is possible if one exploits the information collected from extra inter-sensor RSSI measurements. The target tracking problem is formulated as a kernel matrix completion problem. A probabilistic kernel regression is then proposed that yields a Gaussian likelihood function. The likelihood is used to derive an efficient and accelerated version of the variational filter without resorting to Monte Carlo integration. The proposed data-driven algorithm is, by construction, robust to observation model deviations and adapted to non-stationary environments.

Original language	English
Title of host publication	2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
Pages	2413-2416
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2009.4960108
State	Published - 2009
Event	2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan, Province of China Duration: Apr 19 2009 → Apr 24 2009

Publication series

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

Conference

Conference	2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Country/Territory	Taiwan, Province of China
City	Taipei
Period	04/19/09 → 04/24/09

Keywords

Bayesian filtering
Machine learning
Sensor networks

Access to Document

10.1109/ICASSP.2009.4960108

Cite this

Snoussi, H., Tourneret, J. Y., Djurić, P. M., & Richard, C. (2009). Data-driven online variational filtering inwireless sensor networks. In 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009 (pp. 2413-2416). Article 4960108 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2009.4960108

@inproceedings{910d7d8045fd4e009c8884000644a264,

title = "Data-driven online variational filtering inwireless sensor networks",

abstract = "In this paper, a data-driven extension of the variational algorithm is proposed. Based on a few selected sensors, target tracking is performed distributively without any information about the observation model. Tracking under such conditions is possible if one exploits the information collected from extra inter-sensor RSSI measurements. The target tracking problem is formulated as a kernel matrix completion problem. A probabilistic kernel regression is then proposed that yields a Gaussian likelihood function. The likelihood is used to derive an efficient and accelerated version of the variational filter without resorting to Monte Carlo integration. The proposed data-driven algorithm is, by construction, robust to observation model deviations and adapted to non-stationary environments.",

keywords = "Bayesian filtering, Machine learning, Sensor networks",

author = "Hichem Snoussi and Tourneret, \{Jean Yves\} and Djuri{\'c}, \{Petar M.\} and C{\'e}dric Richard",

year = "2009",

doi = "10.1109/ICASSP.2009.4960108",

language = "English",

isbn = "9781424423545",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

pages = "2413--2416",

booktitle = "2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009",

note = "2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 ; Conference date: 19-04-2009 Through 24-04-2009",

}

Snoussi, H, Tourneret, JY, Djurić, PM & Richard, C 2009, Data-driven online variational filtering inwireless sensor networks. in 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009., 4960108, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 2413-2416, 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, Taipei, Taiwan, Province of China, 04/19/09. https://doi.org/10.1109/ICASSP.2009.4960108

Data-driven online variational filtering inwireless sensor networks. / Snoussi, Hichem; Tourneret, Jean Yves; Djurić, Petar M. et al.
2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009. 2009. p. 2413-2416 4960108 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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

TY - GEN

T1 - Data-driven online variational filtering inwireless sensor networks

AU - Snoussi, Hichem

AU - Tourneret, Jean Yves

AU - Djurić, Petar M.

AU - Richard, Cédric

PY - 2009

Y1 - 2009

N2 - In this paper, a data-driven extension of the variational algorithm is proposed. Based on a few selected sensors, target tracking is performed distributively without any information about the observation model. Tracking under such conditions is possible if one exploits the information collected from extra inter-sensor RSSI measurements. The target tracking problem is formulated as a kernel matrix completion problem. A probabilistic kernel regression is then proposed that yields a Gaussian likelihood function. The likelihood is used to derive an efficient and accelerated version of the variational filter without resorting to Monte Carlo integration. The proposed data-driven algorithm is, by construction, robust to observation model deviations and adapted to non-stationary environments.

AB - In this paper, a data-driven extension of the variational algorithm is proposed. Based on a few selected sensors, target tracking is performed distributively without any information about the observation model. Tracking under such conditions is possible if one exploits the information collected from extra inter-sensor RSSI measurements. The target tracking problem is formulated as a kernel matrix completion problem. A probabilistic kernel regression is then proposed that yields a Gaussian likelihood function. The likelihood is used to derive an efficient and accelerated version of the variational filter without resorting to Monte Carlo integration. The proposed data-driven algorithm is, by construction, robust to observation model deviations and adapted to non-stationary environments.

KW - Bayesian filtering

KW - Machine learning

KW - Sensor networks

UR - https://www.scopus.com/pages/publications/70349220871

U2 - 10.1109/ICASSP.2009.4960108

DO - 10.1109/ICASSP.2009.4960108

M3 - Conference contribution

AN - SCOPUS:70349220871

SN - 9781424423545

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 2413

EP - 2416

BT - 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009

T2 - 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009

Y2 - 19 April 2009 through 24 April 2009

ER -

Snoussi H, Tourneret JY, Djurić PM, Richard C. Data-driven online variational filtering inwireless sensor networks. In 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009. 2009. p. 2413-2416. 4960108. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2009.4960108

Data-driven online variational filtering inwireless sensor networks

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this