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: This paper presents a normalized subband adaptive
filtering (NSAF) algorithm to cope with the sparsity condition of
an underlying system in the context of compressive sensing. By
regularizing a weighted l1-norm of the filter taps estimate onto the
cost function of the NSAF and utilizing a subgradient analysis,
the update recursion of the l1-norm constraint NSAF is derived.
Considering two distinct weighted l1-norm regularization cases, two
versions of the l1-norm constraint NSAF are presented. Simulation
results clearly indicate the superior performance of the proposed
l1-norm constraint NSAFs comparing with the classical NSAF.