Fusion of multiple landmine detection algorithms using an adaptive neuro fuzzy inference system

We present a fusion method, based on fuzzy inference, for detecting buried objects using ground-penetrating radar (GPR) data. The performance of different discrimination algorithms can vary significantly depending on the target type, burial orientation, and other environmental conditions. In some cases, algorithms can provide complementary evidence, while in other cases they can provide contradicting evidence. Thus, effective fusion of these algorithms can achieve higher probability of detection with fewer false alarms. The proposed fusion method is based on an Adaptive Neuro Fuzzy Inference System (ANFIS) [1] capable of simultaneously identifying local contexts as well as learning optimal weights for combining local expert discriminators. It is capable of learning meaningful and simple fuzzy rules for different regions of the input space. Results on large and diverse GPR data collections show that the proposed fusion approach can identify local, simple, and meaningful rules capable of non-linear fusion of different discriminators. We also show that the proposed fuzzy inference outperforms other commonly used fusion methods.