Comparison of predetection and postdetection fusion for mine detection

We present and compare methods for pre-detection and post- detection fusion of multi-sensor data. This study emphasis methods suitable for data that are non-commensurate and sampled at non-coincident points. Decision-level fusion is most convenient for such data, but this approach is sub- optimal in principle, since targets not detected by all sensor will not achieve the maximum benefits of fusion. A novel feature-level fusion algorithm for these conditions is described. The optimal forms of both decision-level and feature-level fusion are described, and some approximations are reviewed. Preliminary result for these two fusion techniques are presented for experimental data acquired by a metal detector, a ground-penetrating radar, and an IR camera.