Automatic modulation classification (AMC) plays a key role in cognitive radio. For AMC, convolutional neural networks (CNNs) have been explored in previous works extensively and deliver the best performance. However, temporal dependencies of signals modeled by CNNs are inherently implicit and insufficient. As a result, models need more data to learn discriminative features automatically. In this work, we propose a hybrid model named HybridNet, where a bidirectional gated recurrent unit (Bi-GRU) is placed after CNN to capture temporal dependencies explicitly. In addition, we investigate why varying Signal-to-Noise Ratio (SNR) dataset makes performance deteriorate. By visualization, we discover that the increase of the intra-class divergence under sharply varying SNR is the central cause. To this end, channel-wise attention is adopted in HybridNet to learn different patterns existing in SNR, which does not require SNR labels in the training process or inference values of SNR. On RadioML2016.10b, our HybridNet obtains the best accuracy among all scales of training data. Especially, in small datasets, our model obtains 87.4% accuracy that is 9.7% higher than the baseline method.

With the progress of signal processing technology and the emergence of new system radars, the space electromagnetic environment becomes more and more complex, which puts forward higher requirements for the deinterleaving method of radar signals. Traditional signal deinterleaving algorithms rely heavily on manual experience threshold and have poor robustness. To address this problem, we designed an intelligent radar signal deinterleaving algorithm that was completed by encoding the frequency characteristic matrix and semantic segmentation network, named Sep-RefineNet. The frequency characteristic matrix can well construct the semantic features of different pulse streams of radar signals. The Sep-RefineNet semantic segmentation network can complete pixel-level segmentation of the frequency characteristic matrix and finally uses position decoding and verification to obtain the position in the original pulse stream to complete radar signals deinterleaving. The proposed method avoids the processing of threshold judgment and pulse sequence search in traditional methods. The results of the experiment show that this algorithm improves the deinterleaving accuracy and has a good against-noise ability of aliasing pulses and missing pulses.

With the development of signal processing technology and the use of new radar systems, signal aliasing and electronic interference have occurred in space. The electromagnetic signals have become extremely complicated in their current applications in space, causing difficult problems in terms of accurately identifying radar-modulated signals in low signal-to-noise ratio (SNR) environments. To address this problem, in this paper, we propose an intelligent recognition method that combines time–frequency (T–F) analysis and a deep neural network to identify radar modulation signals. The T–F analysis of the complex Morlet wavelet transform (CMWT) method is used to extract the characteristics of signals and obtain the T–F images. Adaptive filtering and morphological processing are used in T–F image enhancement to reduce the interference of noise on signal characteristics. A deep neural network with the channel-separable ResNet (Sep-ResNet) is used to classify enhanced T–F images. The proposed method completes high-accuracy intelligent recognition of radar-modulated signals in a low-SNR environment. When the SNR is −10 dB, the probability of successful recognition (PSR) is 93.44%.

In this paper, we introduce the fuzzy Mellin transform and investigate some of its operator properties. We then establish the connections with the two-side fuzzy Laplace transform. By using a fuzzy Mellin transform, we solve some fuzzy differential equations under strongly generalized differentiable conditions. Finally, some simple applications are given.

We consider the initial value problem of quaternion fuzzy fractional differential equations in the generalized regular fuzzy function space. By the associate space theorem and fixed point theorem, some results on the existence and stability for the solution of the initial value problem are given. By the fixed point method, we also prove the Hyers–Ulam stability for the solution of the abstract Cauchy problem under some suitable conditions.