  Greedy algorithm are in widespread use for sparse recovery because of its
efficiency. But some evident flaws exists in most popular greedy algorithms,
such as CoSaMP, which includes unreasonable demands on prior knowledge of
target signal and excessive sensitivity to random noise. A new greedy algorithm
called AMOP is proposed in this paper to overcome these obstacles. Unlike
CoSaMP, AMOP can extract necessary information of target signal from sample
data adaptively and operate normally with little prior knowledge. The recovery
error of AMOP is well controlled when random noise is presented and fades away
along with increase of SNR. Moreover, AMOP has good robustness on detailed
setting of target signal and less dependence on structure of measurement
matrix. The validity of AMOP is verified by theoretical derivation. Extensive
simulation experiment is performed to illustrate the advantages of AMOP over
CoSaMP in many respects. AMOP is a good candidate of practical greedy algorithm
in various applications of Compressed Sensing.
