期刊
IEEE SIGNAL PROCESSING LETTERS
卷 21, 期 12, 页码 1476-1480出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2014.2342198
关键词
Alternating direction method of multipliers; convex relaxation; field reconstruction; proximal gradient method; reweighted l(1); sensor selection; sparsity
资金
- U.S. Air Force Office of Scientific Research (AFOSR) [FA9550-10-1-0263, FA9550-10-1-0458]
- National Science Foundation [CNS-1329885, CMMI-0927509]
- Scientific and Technological Research Council of Turkey (TUBITAK) [113E220]
In this letter, a new sparsity-promoting penalty function is introduced for sensor selection problems in field reconstruction, which has the property of avoiding scenarios where the same sensors are successively selected. Using a reweighted l(1) relaxation of the norm, the sensor selection problem is reformulated as a convex quadratic program. In order to handle large-scale problems, we also present two fast algorithms: accelerated proximal gradient method and alternating direction method of multipliers. Numerical results are provided to demonstrate the effectiveness of our approaches.
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