2017/09/10 by Weilin Li, Li, Weilin, Wenjing Liao +1 · 2 citations
Computer Science · Engineering · #42A10 #42A15 #94A08 #94A15 #94A20 #Direction-of-Arrival Estimation Techniques #FOS: Computer and information sciences #Information Theory (cs.IT) #Sparse and Compressive Sensing Techniques #Structural Health Monitoring Techniques
paper · pdf · doi:10.48550/arxiv.1709.03146
openalex publication_date 2017/09/10 · openalex created_date 2017/09/25 · openalex updated_date 2026/07/28
We consider the inverse problem of recovering the locations and amplitudes of a collection of point sources represented as a discrete measure, given M+1 of its noisy low-frequency Fourier coefficients. Super-resolution refers to a stable recovery when the distance Δ between the two closest point sources is less than 1/M. We introduce a clumps model where the point sources are closely spaced within several clumps. Under this assumption, we derive a non-asymptotic lower bound for the minimum singular value of a Vandermonde matrix whose nodes are determined by the point sources. Our estimate is given as a weighted ℓ2 sum, where each term only depends on the configuration of each individual clump. The main novelty is that our lower bound obtains an exact dependence on the \it Super-Resolution Factor SRF=(MΔ)-1. As noise level increases, the \it sensitivity of the noise-space correlation function in the MUSIC algorithm degrades according to a power law in SRF where the exponent depends on the cardinality of the largest clump. Numerical experiments validate our theoretical bounds for the minimum singular value and the sensitivity of MUSIC. We also provide lower and upper bounds for a min-max error of super-resolution for the grid model, which in turn is closely related to the minimum singular value of Vandermonde matrices.