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Open AccessArticle

Emitter Signal Waveform Classification Based on Autocorrelation and Time-Frequency Analysis

1
College of Physical Science and Technology, Central China Normal University, No. 152 Luoyu Road, Wuhan 430079, China
2
Department of Electronic Technology, Naval University of Engineering, Wuhan 430033, China
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(12), 1419; https://doi.org/10.3390/electronics8121419
Received: 30 October 2019 / Revised: 21 November 2019 / Accepted: 25 November 2019 / Published: 28 November 2019
(This article belongs to the Section Circuit and Signal Processing)
Emitter signal waveform recognition and classification are necessary survival techniques in electronic warfare systems. The emitters use various techniques for power management and complex intra-pulse modulations, which can create what looks like a noisy signal to an intercept receiver, so emitter signal waveform recognition at a low signal-to-noise ratio (SNR) has gained increased attention. In this study, we propose an autocorrelation feature image construction technique (ACFICT) combined with a convolutional neural network (CNN) to maintain the unique feature of each signal, and a structure optimization for CNN input layer called hybrid model is designed to achieve image enhancement of the signal autocorrelation, which is different from using a single image combined with CNN to complete classification. We demonstrate the performance of ACFICT by comparing feature images generated by different signal pre-processing algorithms, and the evaluation indicators are signal recognition rate, image stability degree, and image restoration degree. This paper simulates six types of the signals by combining ACFICT with three types of hybrid model, the simulation results compared with the literature show that the proposed methods not only has a high universality, but also better adapts to waveform recognition at low SNR environment. When the SNR is –6 dB, the overall recognition rate of the method reaches 88%. View Full-Text
Keywords: emitter signal waveform recognition; autocorrelation; feature image; hybrid model; low SNR emitter signal waveform recognition; autocorrelation; feature image; hybrid model; low SNR
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Ma, Z.; Huang, Z.; Lin, A.; Huang, G. Emitter Signal Waveform Classification Based on Autocorrelation and Time-Frequency Analysis. Electronics 2019, 8, 1419.

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