Comparison of analysis methods for separating and recognizing multicomponent radar signals

This paper proposes a model for separating and recognizing a mixture of radar signals using combined multiresolution methods and convolution neural networks. The model involves three main steps: separating the signal into individual components using the Multiresolution analysis (MRA) methods: Empirical mode decomposition (EMD), Variational mode decomposition (VMD), and Maximal overlap discrete wavelet packet transform (MODWPT); transforming these components into the time-frequency domain using Wigner-Ville distribution (WVD) and storing them as images; and then feeding these images into the SqueezeNet for recognition. These multiresolution methods are then compared based on three criteria: The number of successful separations, the SNR ratio of the input signal, and the correlation between the separated signal components and the original signal components. Additionally, we evaluate the performance of the SqueezeNet with real-time signals.