GPR detection of buried symmetrically shaped minelike objects using selective independent component analysis

Abstract

This paper addresses the detection of mine-like objects in stepped-frequency ground penetrating radar (SF-GPR) data as a function of object size, object content, and burial depth. The detection approach is based on a Selective Independent Component Analysis (SICA). SICA provides an automatic ranking of components, which enables the suppression of clutter, hence extraction of components carrying mine information. The goal of the investigation is to evaluate various time and frequency domain ICA approaches based on SICA. The performance comparison is based on a series of mine-like objects ranging from small-scale anti-personal (AP) mines to largescale anti-tank (AT) mines. Large-scale SF-GPR measurements on this series of mine-like objects buried in soil were performed. The SF-GPR data was acquired using a wideband monostatic bow-tie antenna operating in the frequency range 750 MHz − 3.0 GHz. The detection and clutter reduction approaches based on SICA are successfully evaluated on this SF-GPR dataset.

GPR Target Detection by Joint Sparse and Low-Rank Matrix Decomposition

“…In the SVD and PCA methods, the number of components spanning the clutter subspace was manually varied from 1 to 10. In the ICA method [25], SVD was firstly used to pre-whiten the B-scan. Then, the FASTICA algorithm [56] was applied to determine the mixing matrix and the independent components.…”

“…On the other hand, subspace approaches model the ground surface reflections as a low-rank subspace based on the observations that the ground surface reflections are stronger than the target reflections, and they are highly correlated among the signals received across the antenna array. Therefore, several subspace decomposition techniques, such as singular value decomposition (SVD) [18]- [21], principal component analysis (PCA) [22]- [24], and independent component analysis (ICA) [25]- [28], have been employed to decompose the radar signal into three different components: clutter, target, and noise. The SVD and PCA based techniques assume the clutter lies in a subspace spanned by the dominant eigencomponents, whereas the ICA based approach considers the clutter to be captured by independent components having Gaussian characteristics.…”

Information Theoretic Criterion Based Clutter Reduction for Ground Penetrating Radar

“…The components that contain targets are selected while discarding the noise and the clutter subspaces. The limitation is selection of target components by visual inspectio [22][23][24][25][26][27][28][29]. To overcome the problem of automatic selection of target subspaces, we propose a scheme based on Information Theoretic Criterion (ITC) and SVD.…”

“…Recently, statistical methods based on Singular Value Decomposition (SVD), principal component analysis and independent component analysis have been explored for clutter reduction [3,[22][23][24][25][26][27][28][29]. These methods explore the statistical properties of the received data and decompose it into different subspaces: target, clutter and noise.…”