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Telecommunications and Radio Engineering

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ISSN Druckformat: 0040-2508

ISSN Online: 1943-6009

SJR: 0.185 SNIP: 0.268 CiteScore™:: 1.5 H-Index: 22

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AUTOMATIC RECOGNITION OF RADAR SIGNAL TYPES BASED ON CNN-LSTM

Volumen 79, Ausgabe 4, 2020, pp. 305-321
DOI: 10.1615/TelecomRadEng.v79.i4.40
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ABSTRAKT

In the field of cognitive electronic warfare, automatic feature learning and recognition of radar signal is an important technology to ensure intelligence reconnaissance. This paper analyses a novel structure of CNN-LSTM and proposes an automatic recognition algorithm for radar signals. The main contributions are as follows: Firstly, the radar signal is transformed into a time-frequency image, and the principal component information of the image is extracted by the proposed image processing method (clipping-marginal frequency interception-binarization-remodeling). Then, the designed network CNN-LSTM is employed to realize self-learning and image category annotation (automatic recognition of signal types). In this network, CNN can extract spatial characteristics, LSTM can extract temporal characteristics, CNN-LSTM can utilize temporal and spatial characteristics at the same time. The simulation results show that the proposed algorithms can effectively identify eight kinds of radar signals in low signal-to-noise ratio (SNR).

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REFERENZIERT VON
  1. Bhatti Sidra Ghayour, Bhatti Aamer Iqbal, Radar Signals Intrapulse Modulation Recognition Using Phase-Based STFT and BiLSTM, IEEE Access, 10, 2022. Crossref

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