High-accuracy automatic target recognition scheme based on a photonic analog-to-digital converter and a convolutional neural network
Jun Wan, Shaofu Xu, Weiwen Zou
Abstract
We propose a high-accuracy automatic target recognition (ATR) scheme based on a photonic analog-to-digital converter (PADC) and a convolutional neural network (CNN). The adoption of the PADC enables wideband signal processing up to several gigahertz, and thus high-resolution range profiles (RPs) are attained. The CNN guarantees high recognition accuracy based on such RPs. With four centimeter-sized objects as targets, the performance of the proposed ATR scheme based on the PADC and CNN is experimentally tested in different range resolution cases. The recognition result reveals that high-range resolution leads to high accuracy of ATR. It is proved that when dealing with centimeter-sized targets, the ATR scheme can acquire a much better recognition accuracy than other RP ATR solutions based on electronic schemes. Analysis results also show the reason why higher recognition accuracy is attained with higher-resolution RPs.