ISAR Image Formation Using Sequential Minimization of L0 and L2 Norms

TL;DR

This paper introduces a sparsity-driven ISAR imaging algorithm that jointly estimates target rotation and sparse power distribution by sequentially minimizing L0 and L2 norms, improving image sparsity and accuracy.

Contribution

It presents a novel method combining L0 and L2 norm minimization for enhanced ISAR image formation through joint parameter estimation.

Findings

Achieves sparser ISAR images with reduced recovery error.

Demonstrates improved target rotation estimation accuracy.

Outperforms existing methods in image clarity and precision.

Abstract

A sparsity-driven algorithm of inverse synthetic aperture radar (ISAR) imaging is proposed. Based on the parametric sparse representation of the received ISAR signal, the problem of ISAR image formation is converted into the joint estimation of the target rotation rate and the sparse power distribution in the spatial domain. This goal is achieved by sequential minimization of L0 and L2 norms, which ensure the sparsest ISAR image and the minimum recovery error, respectively.