Title :
Computable performance analysis of block-sparsity recovery
Author :
Tang, Gongguo ; Nehorai, Arye
Author_Institution :
Preston M. Green Dept. of Electr. & Syst. Eng., Washington Univ. in St. Louis, St. Louis, MO, USA
Abstract :
In this paper, we employ fixed-point iteration and semidefinite programming to compute performance bounds on the basis pursuit algorithm for block-sparsity recovery. As a prerequisite for optimal sensing matrix design, computable performance bounds would open doors for wide applications in sensor arrays, MIMO radar, DNA microarrays, and many other areas where block-sparsity arises naturally.
Keywords :
MIMO communication; mathematical programming; DNA microarrays; MIMO radar; basis pursuit algorithm; block-sparsity recovery; computable performance analysis; fixed-point iteration; optimal sensing matrix design; semidefinite programming; sensor arrays; Indexes; MIMO radar; Optimization; Programming; Sensors; Vectors; block-sparse signal recovery; compressive sensing; computable performance analysis; fixed-point iteration; semidefinite programming; verifiable sufficient condition;
Conference_Titel :
Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2011 4th IEEE International Workshop on
Conference_Location :
San Juan
Print_ISBN :
978-1-4577-2104-5
DOI :
10.1109/CAMSAP.2011.6136000
