Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for sparse signal recovery in compressed sensing (CS). Prior work on OMP, however, has only provided reconstruction guarantees under the assumption that the columns of the CS matrix have equal norms, which is unrealistic in many practical CS applications due to hardware constraints. In this paper, we derive sparse recovery guarantees with OMP, when the CS matrix has unequal column norms. Finally, we show that CS matrices whose column norms are comparable achieve tight guarantees for the successful recovery of the support of a sparse signal and a low mean squared error in the estimate.
|Title of host publication||Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023|
|Publication status||Published - 2023|
|Event||22nd IEEE Statistical Signal Processing Workshop, SSP 2023 - Hanoi, Viet Nam|
Duration: 2 Jul 2023 → 5 Jul 2023
|Conference||22nd IEEE Statistical Signal Processing Workshop, SSP 2023|
|Period||2/07/23 → 5/07/23|
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- Compressive sensing
- mutual coherence
- orthogonal matching pursuit
- support recovery