This paper introduces a deep learning (DL)-based Automatic Modulation Classification
(AMC) model. Our model is considered to be a receiver with a modulation classifier that is …

The increasingly complex radio environment may cause the received low probability of
intercept (LPI) radar signals to overlap in time–frequency domains. Analyzing overlapping …

Automatic modulation classification (AMC) using convolutional neural networks (CNNs) is
an active area of research that has the potential to improve the efficiency and reliability of …

In this paper, we address the challenge of low recognition rates in existing methods for radar
signals from unmanned aerial vehicles (UAV) with low signal-to-noise ratios (SNRs). To …

The recognition and estimation of frequency-modulated continuous-wave (FMCW) radar
signals are critical for both military electronic countermeasures and civilian autonomous …

Abstract Automatic Modulation Classification (AMC) constitutes a fundamental technology
for enabling automatic demodulation in Cognitive Communication Systems (CCS). Due to …

Following the great success of deep learning in signal processing, Many models based on
real-valued convolutional neural networks (CNNs) and recurrent neural networks (RNNs) …

In future electronic warfare (EW), there will be many unmanned aerial vehicles (UAVs)
equipped with electronic support measure (ESM) systems, which will often encounter the …

Signal recognition technology is a currently active area in both civilian and military
applications. Recently, deep learning has aroused extensive attempts in radar signal …

Currently, deep learning methods with stacking small size convolutional filters are widely
used for automatic modulation classification (AMC). In this report, we find some experienced …