Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 64, Issue 4, Pages 995-1006Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2015.2493987
Keywords
Continuous compressed sensing (CCS); DOA estimation; frequency estimation; gridless sparse method; high resolution; reweighted atomic norm minimization (RAM)
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Funding
- Ministry of Education, Republic of Singapore, under Grant AcRF TIER [1 RG78/15]
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The mathematical theory of super-resolution developed recently by Candes and Fernandes-Granda states that a continuous, sparse frequency spectrum can be recovered with infinite precision via a (convex) atomic norm technique given a set of uniform time-space samples. This theory was then extended to the cases of partial/compressive samples and/or multiple measurement vectors via atomic norm minimization (ANM), known as off-grid/continuous compressed sensing (CCS). However, a major problem of existing atomic norm methods is that the frequencies can be recovered only if they are sufficiently separated, prohibiting commonly known high resolution. In this paper, a novel (nonconvex) sparse metric is proposed that promotes sparsity to a greater extent than the atomic norm. Using this metric an optimization problem is formulated and a locally convergent iterative algorithm is implemented. The algorithm iteratively carries out ANM with a sound reweighting strategy which enhances sparsity and resolution, and is termed as reweighted atomic-norm minimization (RAM). Extensive numerical simulations are carried out to demonstrate the advantageous performance of RAM with application to direction of arrival (DOA) estimation.
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