Radar Emitter Identification Based on Improved Convolutional Neural Network
Aiming at the problem that the traditional pulse description word is difficult to effectively identify complex radar emitters, a method of extracting time-frequency characteristics of radar emitters and using improved convolutional neural network for recognition is proposed. Firstly, two-dimensional time-frequency images are obtained by time-frequency analysis of radar signals. Then, the convolution layer of convolutional neural network is used to extract time-frequency features, the full connection layer is used to integrate features, and softmax is used for classification and recognition. Five kinds of common radar signals are identified by simulation experiments, which proves the validity of this method.
We present a computer simulation study of thin crystalline multilayers constructed from two FCC solids with differing lattice constants and binding energies. Both materials are described by Lennard-Jones interatomic potentials and initially have the same orientation and coherent interfaces. We have studied systems in which interfaces are perpendicular to the common [100] and [111] directions, respectively. A novel technique for analyzing local atomic ordering, Common Neighbor Analysis, is used to identify structural characteristics in these systems. We have found several structural changes in the layers of smaller atoms, including an FCC to HCP transition. In a system with (111) texture, a coherent interface to incoherent interface transformation is observed. Calculations of elastic constants of these multilayer structures show that elastic anomalies are associated with the structural variations.