High-Resolution Imaging for Impulse-Based Forward-Looking Ground Penetrating Radar

Abstract

Forward-Looking Ground Penetrating Radar (FLGPR) has multiple applications, one of which includes its use for detecting landmines and other buried improvised explosive devices (IEDs). The standard method for generating synthetic aperture radar (SAR) images for this radar is the backprojection (BP) algorithm, which has poor resolution and high sidelobe problems. In this paper, we consider using the Sparse Iterative Covariance-based Estimation (SPICE) algorithm and the Spare Learning via Iterative Minimization (SLIM) algorithm for generating sparse high-resolution images for FLGPR. A pre-processing step, which involves an orthogonal projection of the received data onto a subspace related to the region of interest is performed, for decreasing the dimension of the data and for clutter reduction. The SLIM and SPICE algorithms are user-parameter free, and are capable of providing SAR images with improved resolution. We also use the well-known CLEAN approach for imaging based on a proposed signal model in the time domain. We show using simulated data that the SPICE and SLIM algorithms provide higher resolution than CLEAN and the standard BP. Imaging using real data collected via the Synchronous Impulse Reconstruction (SIRE) radar, a multiple-input multiple-output (MIMO) FLGPR radar developed by the Army Research Laboratory (ARL), is also presented and used for analysis.