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dc.contributor.authorCoutino, Mario
dc.contributor.authorChepuri, Sundeep Prabhakar
dc.contributor.authorLeus, Geert
dc.date.accessioned2018-11-11T08:54:53Z
dc.date.available2018-11-11T08:54:53Z
dc.date.issued2018-03-12
dc.identifier.citationCoutino M, Chepuri SP, Leus G (2017) Sparse sensing for composite matched subspace detection. 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). Available: http://dx.doi.org/10.1109/camsap.2017.8313125.
dc.identifier.doi10.1109/camsap.2017.8313125
dc.identifier.urihttp://hdl.handle.net/10754/629754
dc.description.abstractIn this paper, we propose sensor selection strategies, based on convex and greedy approaches, for designing sparse samplers for composite detection. Particularly, we focus our attention on sparse samplers for matched subspace detectors. Differently from previous works, that mostly rely on random matrices to perform compression of the sub-spaces, we show how deterministic samplers can be designed under a Neyman-Pearson-like setting when the generalized likelihood ratio test is used. For a less stringent case than the worst case design, we introduce a submodular cost that obtains comparable results with its convex counterpart, while having a linear time heuristic for its near optimal maximization.
dc.description.sponsorshipThis research is supported in part by the ASPIRE project (project 14926 within the STWOTP programme), financed by the Netherlands Organization for Scientific Research (NWO), and the KAUST-MIT-TUD consortium under grant OSR-2015-Sensors-2700. Mario Coutino is partially supported by CONACYT.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectcomposite hypothesis testing
dc.subjectconvex optimization
dc.subjectmatched subspace detector
dc.subjectsensor selection
dc.subjectsubmodular optimization
dc.titleSparse sensing for composite matched subspace detection
dc.typeConference Paper
dc.identifier.journal2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
dc.contributor.institutionDelft University of Technology, Delft, The Netherlands
kaust.grant.numberOSR-2015-Sensors-2700


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