Optimal dimensionality reduction for multi-sensor fusion in the presence of fading and noise

Ioannis D. Schizas, Georgios B Giannakis, Zhi-Quan Luo

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

7 Scopus citations

Abstract

We derive linear estimators of stationary random signals based on reduced-dimensionality observations collected at distributed sensors and communicated over wireless fading links to a fusion center, where additive noise is also present. Dimensionality reduction compresses sensor data to meet low-power and bandwidth constraints, while linearity in compression and estimation are well motivated by the limited computing capabilities wireless sensor networks are envisioned to operate with. For uncorrelated sensor data, we develop mean-square error (MSB) optimal estimators in closed-form; while for correlated sensor data, we derive sub-optimal iterative estimators which guarantee convergence at least to a stationary point. Performance analysis and corroborating simulations demonstrate the merits of the novel distributed estimators relative to existing alternatives.

Original languageEnglish (US)
Title of host publication2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
Volume4
StatePublished - Dec 1 2006
Event2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France
Duration: May 14 2006May 19 2006

Other

Other2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
CountryFrance
CityToulouse
Period5/14/065/19/06

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    Schizas, I. D., Giannakis, G. B., & Luo, Z-Q. (2006). Optimal dimensionality reduction for multi-sensor fusion in the presence of fading and noise. In 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings (Vol. 4). [1661107]