Compressed sensing of Gauss-Markov random field with wireless sensor networks

TitleCompressed sensing of Gauss-Markov random field with wireless sensor networks
Publication TypeConference Paper
Year of Publication2008
AuthorsOka, A., and L. Lampe
Conference NameSensor Array and Multichannel Signal Processing Workshop, 2008. SAM 2008. 5th IEEE
Pagination257 -260
Date Publishedjul.
Keywordsa-priori statistical information, channel coding, compressed sensing, fusion center, Gauss-Markov random fields, Gaussian processes, intersensor communication, Markov processes, matrix algebra, matrix sensing, random processes, reconstruction algorithms, sensor arrays, sensor fusion, sensors array, statistical analysis, statistical model, wireless sensor networks

We propose a scalable and energy efficient method for reconstructing a dasiasparsepsila Gauss-Markov random field that is observed by an array of sensors and described over wireless channels to a fusion center. The encoder is universal, i.e. invariant to the statistical model of the source and the channel, and is based on compressed sensing. The reconstruction algorithms exploit the a-priori statistical information about the field and the channel at the fusion center to yield a performance comparable to information theoretic bounds. Furthermore, by putting stringent constraints on the sensing matrix we avoid (or even eliminate) inter-sensor communication while suffering negligible degradation in performance.


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