Application Of Neural Network Algorithm In The Detection Of Electromagnetic Properties And Identification Of Electromagnetic Objects

Neural network algorithms have significant application potential in handling electromagnetic problems,especially in the detection of electromagnetic properties and identification of electromagnetic objects.Two typical problems are investigated here,one is applying the neural network algorithm in the detection of failure elements in phased arrays,and the other is the identification of a digit-shape radiator consisting of halfwave dipoles.Array antennas can realize required radiation pattern by controlling the feeding of elements,hence are widely used in radar,communication and other systems.Phased array antennas have the advantage of scanning the beam to a specific angle through phase shifters.If a failure element appears in the array,it will cause the decline of radiation pattern and even affect performance of the antenna.The neural network algorithm can bypass the complicated antenna measurements and calculation process,and extract features from the data sampled from far-field radiation pattern of the phased array antenna.The neural network establishes a mapping relationship between the far-field data and the corresponding number and location of the failure elements.The results show that the neural network algorithm can effectively diagnose the failures of the planar array with an accuracy rate of96%.Half-wave dipole antennas have been widely used in many communication and radar systems.Here,a digit-shape radiator composed of half-wave dipoles is designed based on HFSS simulation.The far-field electric field data of the radiator is sampled in free space.The neural network is trained to obtain a pre-trained model,which can identify the target well corresponding to the electric field.Considering the practical application scenarios as well as the transferability of neural network models,a metal box with a hole is added outside the digit-shape radiator.Besides,the digitshape radiator is placed in a dielectric box with a certain thickness,and the far-field electric field is sampled under these two conditions.The pre-trained model can still identify the target after applying the transfer methods.Here,the neural network algorithm is successfully applied to electromagnetic target recognition tasks.By applying the feature matching method of transfer learning,an attempt is made to determine which features of the pretrained model are useful for learning target tasks and which layers should be transferred from the hierarchical perspective of the neural network.The examples verify that the features of the first two layers(bottom layers)of the pre-trained model are more common.By transferring the bottom layers,the computing costs will be saved.In fact,applying the transfer learning methods is very efficient in saving time and improving recognition efficiency.