Abstract
To cope with the complex electromagnetic environment and varied signal styles, a novel method based on the energy cumulant of short time Fourier transform and reinforced deep belief network is proposed to gain a higher correct recognition rate for radar emitter intra-pulse signals at a low signal-to-noise ratio. The energy cumulant of short time Fourier transform is attained by calculating the accumulations of each frequency sample value with the different time samples. Before this procedure, the time frequency distribution via short time Fourier transform is processed by base noise reduction. The reinforced deep belief network is proposed to employ the input feature vectors for training to achieve the radar emitter recognition and classification. Simulation results manifest that the proposed method is feasible and robust in radar emitter recognition even at a low SNR.
Highlights
Radar emitter recognition is an important part in radar reconnaissance and confrontation systems, which is on top of the radar signal sorting [1] and leads the recognition [2,3], location [4], and tracking [5] tasks
The recognition results directly influence the performance of a radar reconnaissance system, so, a lot of recognition studies have been done centered on the five common parameters of radar: radio frequency (RF), time of arrival (TOA), pulse amplitude (PA), pulse width (PW) and angle of arrival (AOA) [6,7,8]
A method based on EC-short time Fourier transform (STFT) and reinforced deep belief network (RDBN) was proposed for radar emitter intra-pulse
Summary
Radar emitter recognition is an important part in radar reconnaissance and confrontation systems, which is on top of the radar signal sorting [1] and leads the recognition [2,3], location [4], and tracking [5] tasks. The recognition results directly influence the performance of a radar reconnaissance system, so, a lot of recognition studies have been done centered on the five common parameters of radar: radio frequency (RF), time of arrival (TOA), pulse amplitude (PA), pulse width (PW) and angle of arrival (AOA) [6,7,8]. While the electromagnetic environment is more and more adverse, and the signal system becomes more and more complicated, it is not enough to complete the radar emitter recognition only depending on common parameter matching methods. Pulse compression radar provides some performance advantages to face the complicated electromagnetic environment and variable signal patterns with its better anti-interference [9]. New radar emitter recognition methods that get higher recognition rates and offer more recognition information by analyzing the intra-pulse signal of pulse compression radars were presented [10,11,12]
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