Comparison of Independent-Component-Analysis (ICA) Algorithms for GPR Detection of Non-Metallic Land Mines

摘要

This paper deals with the detection of non-metallic anti-personnel (AP) land mines using stepped-frequency ground penetrating radar. A class of the so-called Independent Component Analysis (ICA) represents a powerful tool for such a detection. Various ICA algorithms have been introduces in the literature; therefore there is a need to compare these methods. In this contribution, four of the most common ICA methods are studied and compared to each other as regarding their ability to separate the target and clutter signals. These are the extended Infomax, the FastICA, the Joint Approximate Diagonalization of Eigenmatrices (JADE), and the Second Order Blind Identification (SOBI). The four algorithms have been applied to the same data set which has been collected using an SF-GPR. The area under the Receiver Operating Characteristic (ROC) curve has been used to compare the clutter removal efficiency of the different algorithms. All four methods have given approximately consistent results. However both JADE and SOBI methods have shown better performances over Infomax and FastICA.