Distributed Spatio-Temporal Sampling of Diffusion Fields from Sparse Instantaneous Sources

We study the spatio-temporal sampling of a diffusion field driven by K unknown instantaneous source distributions. Exploiting the spatio-temporal correlation offered by the diffusion model, we show that it is possible to compensate for insufficient spatial sampling densities (i.e. sub-Nyquist sampling) by increasing the temporal sampling rate, as long as their product remains roughly a constant. Combining a distributed sparse sampling scheme and an adaptive feedback mechanism, the proposed sampling algorithm can accurately and efficiently estimate the unknown sources and reconstruct the field. The total number of samples to be transmitted through the network is roughly equal to the number of degrees of freedom of the field, plus some additional costs for in-network averaging.


Published in:
Proceedings of the 34d CAMSAP
Presented at:
3rd International Workshop on Computational Advances in Multi-Sensor Adaptative Processing (CAMSAP), Aruba, Netherland Antilles,, December 13-16, 2009
Year:
2009
Publisher:
Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa
Keywords:
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 Record created 2010-06-22, last modified 2018-03-17

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