Micro-Doppler Effects Removed Sparse Aperture ISAR Imaging via Low-Rank and Double Sparsity Constrained ADMM and...

Area of Science:

• Radar Imaging
• Signal Processing
• Electromagnetics

Background:

• Micro-Doppler (m-D) effects degrade Inverse Synthetic Aperture Radar (ISAR) imaging of targets with micro-motion parts.
• Sparse aperture data, due to under-sampling, causes image defocusing and hinders m-D effect removal.
• Existing methods struggle with signal decomposition for m-D effect removal in sparse aperture scenarios.

Purpose of the Study:

• To propose a novel sparse aperture ISAR (SA-ISAR) imaging algorithm for effective m-D effect removal.
• To address the challenges of image defocusing and interference caused by under-sampled data.
• To improve the performance of ISAR imaging for targets with micro-motion.

Main Methods:

• Decomposition of radar echo into main body and micro-motion components based on low-rank and sparse properties of range profiles.
• Utilizing the sparsity of the ISAR image as a constraint to mitigate interference from sparse apertures.
• Modeling the problem as a triply constrained underdetermined optimization problem solved using alternating direction method of multipliers (ADMM) and linearized ADMM (L-ADMM).

Main Results:

• The proposed SA-ISAR algorithm effectively removes m-D effects from sparse aperture data.
• The algorithm successfully decomposes range profiles by exploiting low-rank and sparse characteristics.
• Experimental results with simulated and measured data validate the algorithm's effectiveness in producing focused ISAR images.

Conclusions:

• The developed SA-ISAR imaging algorithm offers a robust solution for targets with micro-motion under sparse aperture conditions.
• The utilization of low-rank, sparse properties, and ADMM/L-ADMM provides an efficient and effective approach to ISAR imaging.
• This work significantly enhances ISAR imaging capabilities in scenarios with under-sampled data and complex target dynamics.