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Implementation of analog signal modulation recognition algorithms from research papers, featuring computation of characteristic parameters including zero-centered normalized instantaneous amplitude spectral density maximum and instantaneous amplitude
Modulation information in communication signals is embedded in variations of instantaneous amplitude, phase, and frequency, with different signals exhibiting unique spectral characteristics. By extracting statistical parameters from instantaneous amplitude, phase, frequency, and spectral features, various communication signals can be identified. Commonly, transformations are applied to instantaneous amplitude, phase, frequency, and power spectral density to derive discriminative feature parameters. In practice, this involves algorithms like Hilbert transform for instantaneous attributes calculation and Fourier analysis for spectral feature extraction.
Traditional modulation recognition algorithms utilize key statistical features including: maximum value of the zero-centered normalized instantaneous amplitude spectral density, standard deviation of the zero-centered normalized instantaneous amplitude absolute value, standard deviation of the absolute value of the nonlinear component in zero-centered non-weak signal segment instantaneous phase, standard deviation of the nonlinear component in zero-centered non-weak signal segment instantaneous phase, and standard deviation of the absolute value of zero-centered normalized non-weak signal segment instantaneous frequency. These features can be computationally extracted using signal processing techniques to enhance classification performance.