In 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.
Coutino 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.
This 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.