Intelligent game of electromagnetic spectrum
ZHANG Wei, WANG Sha-fei, LIN Jing-ran, LI Qiang, SHAO Huai-zong
The artificial intelligence technology(e.g., deep learning) is an effective approach to electromagnetic target(ET) recognition. However, in the recognition of multi-mode ETs, when the training samples with a certain mode are missing or rare, the recognition rate with this mode will be significantly degraded. The reason mainly lies in that the data distance between different modes of the same ET may exceed the data distance between different ETs. To remedy this, a cross-modal ET recognition approach via Siamese network is developed in this paper. Following the framework of metric learning, we design the network structure and the loss function carefully, so that the recognition training process intentionally drives the Siamese network to enlarge the data distance between different ETs while shorten the distance between different modes of the same ET. Consequently, the multi-mode ETs can still be successfully recognized by employing certain distance-based decision criterion, even with imbalanced training data sets for different modes. The numerical results based on realistic data show that with same data sets and network size, the cross-modal recognition rate of the proposed approach is 20% higher than that of the classical convolutional neural network approach, and that of the popular data-enhancement approaches.