A Robust Copula Model for Radar-Based Landmine Detection

We present a robust copula model for landmine detection based on a likelihood ratio test. The test is applied to radar-based imagery from multiple viewpoints of the interrogation area. Different copula density functions are investigated in terms of their effectiveness in incorporating the statistical dependence between multi-view images. The test is designed to maximize the worst-case performance over all feasible mine and clutter distributions. Using numerical radar data of shallow buried targets under varying surface roughness, we demonstrate that the robust copula-based detector outperforms existing approaches and provides a high detection performance for a wide range of false-alarm rates.