Optimal decision fusion in Through-the-Wall Radar Imaging

We consider the problem of target detection behind walls based on optimum decision fusion using Neyman-Pearson tests. A framework, demonstrating the use of multiple sensor platforms and distributed detection for the emerging application area of Through-the-Wall Radar Imaging is presented. We derive the optimum decision rule at the fusion center for three dissimilar sensors and compare the corresponding target detection results to that achieved when using a centralized decision approach. Real data generated using a two dimensional scanning system is used for the performance comparison.