Direction-finding based on the theory of super-resolution in sparse recovery algorithms

Cheng-Yu Hung; Mostafa Kaveh

ICASSP 2015accepted0 citations

Direction-finding based on the theory of super-resolution in sparse recovery algorithms

Abstract

The problem of recovering directions-of-arrival in the sparse signal model with multiple snapshots is considered. Based on the theory of super resolution, multiple snapshots are used to jointly estimate directions-of-arrival in the continuous domain. Instead of uniformly discretizing the search range, interpolation preprocessing on the estimated super-resolution directions is suggested leading to a sparse convex optimization formulation. Moreover, a first order iterative algorithm is employed to reduce the computational time. A good selection of regularization parameter is guaranteed via the modified generalized cross validation (GCV). Numerical results demonstrate the performance of the proposed methods.

BibTeX

@inproceedings{icassp2015_directionfinding,
  title = {Direction-finding based on the theory of super-resolution in sparse recovery algorithms},
  author = {Cheng-Yu Hung and Mostafa Kaveh},
  booktitle = {ICASSP 2015},
  year = {2015}
}