@article {237816,
	title = {Sampling Sparse Signals on the Sphere: Algorithms and Applications},
	journal = {IEEE Transactions on Signal Processing},
	volume = {64},
	number = {1},
	year = {2016},
	month = {2015},
	pages = {189-202},
	abstract = {We propose a sampling scheme that can perfectly reconstruct a collection ofspikes on the sphere from samples of their lowpass-filtered observations.Central to our algorithm is a generalization of the annihilating filtermethod, a tool widely used in array signal processing and finite-rate-of-innovation(FRI) sampling. The proposed algorithm can reconstruct $K$ spikesfrom $(K+\sqrt{K})^2$ spatial samples---a sampling requirement thatimproves over known sparse sampling schemes on the sphere by a factor of upto four.We showcase the versatility of the proposed algorithm by applying it tothree different problems: 1) sampling diffusion processes induced bylocalized sources on the sphere, 2) shot-noise removal, and 3) sound sourcelocalization (SSL) by a spherical microphone array. In particular, we showhow SSL can be reformulated as a spherical sparse sampling problem.},
	url = {http://arxiv.org/abs/1502.07577},
	author = {Ivan Dokmanic and Yue M. Lu}
}
