Abstract
Recognition of Radar Emitters with Agile Waveform Based on Hybrid Deep Neural Network and Attention Mechanism
Highlights
Radar emitter recognition is a key link in radar countermeasures and reconnaissance
Inspired by the above ideas, in order to deal with the problem that the conventional characteristic parameters of radar emitter signals with agile waveform are variable, this paper proposes a recognition method of radar emitters with agile waveform based on hybrid deep neural network and attention mechanism
In order to verify the performance of the method proposed in this paper, we simulated to generate a dataset of radar emitters with agile waveform, including 100 radar emitters, each radar emitter corresponds to 1000 working modes, each mode is a pulse sequence composed of different numbers of pulse description words (PDW)
Summary
Radar emitter recognition is a key link in radar countermeasures and reconnaissance. It extracts the characteristic parameters and working parameters of the radar emitters on the basis of sorting. In the early development of radar emitter recognition technology, due to the relatively simple electronic countermeasure technology and electromagnetic environment, researchers mainly studied the template matching method based on signal characteristic parameters [2]. Inspired by the above ideas, in order to deal with the problem that the conventional characteristic parameters of radar emitter signals with agile waveform are variable, this paper proposes a recognition method of radar emitters with agile waveform based on hybrid deep neural network and attention mechanism. In order to obtain the deep features that can characterize the agility of the waveform, the attention mechanism-based method is used to fuse the extracted structural features and timing features, and at the same time it can reduce the influence of noise in complex electromagnetic environment on the characteristic data of radar emitter. This paper uses the method based on attention mechanism for feature fusion, which can overcome the influence of noise in complex electromagnetic environment
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