Sparsity-Aware Sensor Selection: Centralized and Distributed Algorithms

The selection of the minimum number of sensors within a network to satisfy a certain estimation performance metric is an interesting problem with a plethora of applications. We explore the sparsity embedded within the problem and propose a relaxed sparsity-aware sensor selection approach which is eq...

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Bibliographic Details
Published in:IEEE signal processing letters 2014-02, Vol.21 (2), p.217-220
Main Authors: Jamali-Rad, Hadi, Simonetto, Andrea, Leus, Geert
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Convergence
Distributed algorithms
Distributed estimation
Equivalence
Measurement
Networks
Noise
Optimization
Performance measurement
Robot sensing systems
sensor selection
Sensors
Signal processing
Signal processing algorithms
sparse reconstruction
Vectors
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Summary:The selection of the minimum number of sensors within a network to satisfy a certain estimation performance metric is an interesting problem with a plethora of applications. We explore the sparsity embedded within the problem and propose a relaxed sparsity-aware sensor selection approach which is equivalent to the unrelaxed problem under certain conditions. We also present a reasonably low-complexity and elegant distributed version of the centralized problem with convergence guarantees such that each sensor can decide itself whether it should contribute to the estimation or not. Our simulation results corroborate our claims and illustrate a promising performance for the proposed centralized and distributed algorithms.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2013.2297419