AMC (automatic modulation classification) plays a vital role in spectrum monitoring and electromagnetic abnormal signal detection. Up to now, few studies have focused on the complementarity between features of different modalities and the importance of the feature fusion mechanism in the AMC method. This paper proposes a dual-modal feature fusion convolutional neural network (DMFF-CNN) for AMC to use the complementarity between different modal features fully. DMFF-CNN uses the gram angular field (GAF) image coding and intelligence quotient (IQ) data combined with CNN. Firstly, the original signal is converted into images by GAF, and the GAF images are used as the input of ResNet50. Secondly, it is converted into IQ data and as the complex value network (CV-CNN) input to extract features. Furthermore, a dual-modal feature fusion mechanism (DMFF) is proposed to fuse the dual-modal features extracted by GAF-ResNet50 and CV-CNN. The fusion feature is used as the input of DMFF-CNN for model training to achieve AMC of multi-type signals. In the evaluation stage, the advantages of the DMFF mechanism proposed in this paper and the accuracy improvement compared with other feature fusion algorithms are discussed. The experiment shows that our method performs better than others, including some state-of-the-art methods, and has superior robustness at a low signal-to-noise ratio (SNR), and the average classification accuracy of the dataset signals reaches 92.1%. The DMFF-CNN proposed in this paper provides a new path for the AMC field.
To solve the problem of poor recognition effect of transient signal in low Signal-to-Noise ratio(SNR) and strong interference electromagnetic environment, a morphological filtering method based on the multi-scale combined difference product (MCDPMF) was proposed. This paper concentrates on the issues of sudden changes in transient electronic signal, such as impulses and edges. Firstly, it provides a difference product morphological filter. Moreover, the extended and multi-origin morphological Structural Elements (SEs) is constructed, combining with the multi-structural layers a (a indicates the structural layers of the MCDPMF), the transient electromagnetic weak signal is multi-scale filtered. They are used to optimize the number of the structure layers a adaptively based on the amplitude characteristic ratio of the positive and negative polarity of the filtered signal(HML value), combining the kurtosis-SNR(k x − SN R) ratio characteristic coefficient. MCDPMF is proposed to enhance the filtering results and suppress the noise frequency points. Meanwhile, it can extract the structure components and identify the features of the transient electromagnetic weak signal. It can be shown from simulation and experimental results that the proposed method is superior to EMD, AVG, OCCO, and other methods in subjective evaluation and objective indicators.
In response to the problems of biased estimation of instantaneous frequency (IF) and poor noise immunity in current time-frequency (TF) analysis methods, the Adaptive scale chirplet transform (ASCT) is proposed in this paper. The core idea of the proposed algorithm is to use a frequency-dependent quadratic polynomial kernel function to approximate the IF of the signal and to use the time-varying window length to overcome the frequency resolution problem due to the change in signal modulation. This method can dynamically select suitable parameters and overcome the disadvantage of unfocused energy of TF distribution. The experimental results show that the ASCT algorithm has high TF aggregation and can suppress noise interference well. In practical signal processing, the advantage of the ASCT algorithm is that it can accurately depict the characteristic frequency of the signal and detect the fault in the bearing signal. Both simulation and experimental results prove the strong realistic relevance of this algorithm.
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