Compressive Sampling Stepped Frequency GPR Using Probabilistic Structured Sparsity Models

Abstract

We investigate a compressive sampling (CS) stepped frequency ground penetrating radar for detection of underground objects, which uses Bayesian estimation and a probabilistic model for the target support. Due to the underground targets being sparse, the B-scan is a sparse image. Using the CS principle, the stepped frequency radar is implemented using a subset of random frequencies at each antenna position. For image reconstruction we use Markov Chain and Markov Random Field models for the target support in the B-scan, where we also estimate the model parameters using the Expectation Maximization algorithm. The approach is tested using Web radar data obtained by measuring the signal responses scattered off land mine targets in a laboratory experimental setup. Our approach results in improved performance compared to the standard denoising algorithm for image reconstruction.