Abstract: In the paper, a fast high-resolution range profile synthetic algorithm called orthogonal matching pursuit with sensing dictionary (OMP-SD) is proposed. It formulates the traditional HRRP synthetic to be a sparse approximation problem over redundant dictionary. As it employs a priori that the synthetic range profile (SRP) of targets are sparse, SRP can be accomplished even in presence of data lost. Besides, the computation complexity decreases from O(MNDK) flops for OMP to O(M(N + D)K) flops for OMP-SD by introducing sensing dictionary (SD). Simulation experiments illustrate its advantages both in additive white Gaussian noise (AWGN) and noiseless situation, respectively.
Abstract: This paper considers a robust recovery of sparse frequencies
from partial phase-only measurements. With the proposed
method, sparse frequencies can be reconstructed, which makes full
use of the sparse distribution in the Fourier representation of the
complex-valued time signal. Simulation experiments illustrate the
proposed method-s advantages over conventional methods in both
noiseless and additive white Gaussian noise cases.