Abstract: High-performance computing (HPC) based emulators can be used to model the scattering from multiple stationary and moving targets for RADAR applications. These emulators rely on the RADAR Cross Section (RCS) of the targets being available in complex scenarios. Representing the RCS using tables generated from EM simulations is oftentimes cumbersome leading to large storage requirements. In this paper, we proposed a spherical harmonic based anisotropic scatterer model to represent the RCS of complex targets. The problem of finding the locations and reflection profiles of all scatterers can be formulated as a linear least square problem with a special sparsity constraint. We solve this problem using a modified Orthogonal Matching Pursuit algorithm. The results show that the spherical harmonic based scatterer model can effectively represent the RCS data of complex targets.
Abstract: A sparse representation speech denoising method based on adapted stopping residue error was presented in this paper. Firstly, the cross-correlation between the clean speech spectrum and the noise spectrum was analyzed, and an estimation method was proposed. In the denoising method, an over-complete dictionary of the clean speech power spectrum was learned with the K-singular value decomposition (K-SVD) algorithm. In the sparse representation stage, the stopping residue error was adaptively achieved according to the estimated cross-correlation and the adjusted noise spectrum, and the orthogonal matching pursuit (OMP) approach was applied to reconstruct the clean speech spectrum from the noisy speech. Finally, the clean speech was re-synthesised via the inverse Fourier transform with the reconstructed speech spectrum and the noisy speech phase. The experiment results show that the proposed method outperforms the conventional methods in terms of subjective and objective measure.
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: 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.