Deep Image Prior Assisted ISAR Imaging for Missing Data Case

TL;DR

This paper introduces a deep image prior approach to complete missing data in ISAR radar signals, significantly improving imaging quality over traditional methods especially at high missing ratios.

Contribution

It proposes a novel DIP-based method for radar data completion that outperforms existing techniques in handling high missing data ratios.

Findings

100% RMSE improvement in extreme cases

50% higher correlation metrics

30% better information content (IC) metrics

Abstract

In Inverse Synthetic Aperture Radar (ISAR), random missing entries of the received radar echo matrix deteriorate the imaging quality, compromising target distinction from the background. Compressive sensing techniques or matrix completion prior to conventional imaging have been used in recent years to solve this issue. However, while the former techniques fail to preserve target continuity due to the sparsity constraint, the latter fails for high missing ratios. This paper proposes to use deep image prior (DIP) to complete the complex radar data and then obtain the radar image by conventional Fourier imaging. Real and imaginary parts are separately completed by independent deep structures and then put together for the imaging part. The proposed DIP based imaging method has been compared with IALM, 2D-SL0 and NNM methods visually and quantitatively for both simulated and real data. The results demonstrate an increase of 100% for some extreme cases in terms of RMSE, 50% increase on Correlation and 30% increase on IC metrics quantitatively.