Sampling Sparse Signals on the Sphere: Algorithms and Applications

Abstract

We propose a sampling scheme that can perfectly reconstruct a collection of spikes on the sphere from samples of their lowpass-filtered observations. Central to our algorithm is a generalization of the annihilating filter method, a tool widely used in array signal processing and finite-rate-of-innovation (FRI) sampling. The proposed algorithm can reconstruct $K$ spikes from $(K+\sqrt{K}{)}^2$ spatial samples. This sampling requirement improves over previously known FRI sampling schemes on the sphere by a factor of four for large $K$. We showcase the versatility of the proposed algorithm by applying it to three different problems: 1) sampling diffusion processes induced by localized sources on the sphere, 2) shot noise removal, and 3) sound source localization (SSL) by a spherical microphone array. In particular, we show how SSL can be reformulated as a spherical sparse sampling problem.

14 pages, 8 figures, submitted to IEEE Transactions on Signal Processing

References in corpus (2)

• Super-resolution on the Sphere using Convex Optimization
• An Optimal-Dimensionality Sampling Scheme on the Sphere with Fast Spherical Harmonic Transforms

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