TY - JFULL
AU - Israa Sh. Tawfic and Sema Koc Kayhan
PY - 2014/11/
TI - Partially Knowing of Least Support Orthogonal Matching Pursuit (PKLS-OMP) for Recovering Signal
T2 - International Journal of Computer and Information Engineering
SP - 1851
EP - 1856
VL - 8
SN - 1307-6892
UR - https://publications.waset.org/pdf/9999597
PU - World Academy of Science, Engineering and Technology
NX - Open Science Index 94, 2014
N2 - Given a large sparse signal, great wishes are to
reconstruct the signal precisely and accurately from lease number of
measurements as possible as it could. Although this seems possible
by theory, the difficulty is in built an algorithm to perform the
accuracy and efficiency of reconstructing. This paper proposes a new
proved method to reconstruct sparse signal depend on using new
method called Least Support Matching Pursuit (LS-OMP) merge it
with the theory of Partial Knowing Support (PSK) given new method
called Partially Knowing of Least Support Orthogonal Matching
Pursuit (PKLS-OMP).
The new methods depend on the greedy algorithm to compute the
support which depends on the number of iterations. So to make it
faster, the PKLS-OMP adds the idea of partial knowing support of its
algorithm. It shows the efficiency, simplicity, and accuracy to get
back the original signal if the sampling matrix satisfies the Restricted
Isometry Property (RIP).
Simulation results also show that it outperforms many algorithms
especially for compressible signals.
ER -