CPN based multi-sensor data fusion for target classification

A counter-propagation network (CPN) based system of multi-sensor data fusion at feature level for target classification is proposed in this paper. This presentation mainly describes the use of the CPN in the data fusion system for target classification, as well as the algorithm used for training the CPN. As a demonstration of the advantages of the CPN, a popular back-propagation network (BPN) and a standard counter-propagation network (SCPN) are investigated at the same time. Finally, to illustrate the effectiveness of the CPN with the modified training algorithm for data fusion at feature level, we present the experiments for the application system based on FLIR and TV camera. The experimental results for the system using the real-world database show that the CPN with the proposed algorithm provides the best overall performance. The classification accuracy, robustness and learning process are significantly improved.