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Radar Emitter Identification Based on Fully Connected Spiking Neural Network
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Abstract
In the face of the increasing complex electromagnetic environment and new radar system, it is difficult to extract radar emitter characteristics based on manual mode to meet requirements of modern cognitive electronic warfare. In order to improve the intelligence level of radar emitter identification, a new method based on Spiking Neuron Network (SNN) for radar emitter identification is proposed in this paper. Firstly, five kinds of common radar signals are converted into two-dimensional gray scale images by using time-frequency analysis method. Then, the images are converted into spikes by Poisson coder, which are put into a fully connected spiking neural network for training and emitter identification. Finally, the simulation results prove the validity of this method by comparing with the traditional neural network.
Title: Radar Emitter Identification Based on Fully Connected Spiking Neural Network
Description:
Abstract
In the face of the increasing complex electromagnetic environment and new radar system, it is difficult to extract radar emitter characteristics based on manual mode to meet requirements of modern cognitive electronic warfare.
In order to improve the intelligence level of radar emitter identification, a new method based on Spiking Neuron Network (SNN) for radar emitter identification is proposed in this paper.
Firstly, five kinds of common radar signals are converted into two-dimensional gray scale images by using time-frequency analysis method.
Then, the images are converted into spikes by Poisson coder, which are put into a fully connected spiking neural network for training and emitter identification.
Finally, the simulation results prove the validity of this method by comparing with the traditional neural network.
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