MUSIC Algorithm Based on Cyclostationary Signals for Impulsive Noise Environments
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In impulsive noise environments, we propose a MUSIC algorithm based on cyclostationary signals. This algorithm employs fractional lower-order cyclostationary statistics to achieve precise signal localization and analysis. Through cyclostationary processing of signals, the method demonstrates enhanced resistance to noise interference, thereby improving the accuracy and reliability of signal processing. The implementation involves computing cyclic autocorrelation functions and applying eigenvalue decomposition to the covariance matrix constructed from fractional lower-order moments. This research holds significant importance for the advancement of signal processing techniques, particularly in challenging noise conditions where conventional methods may fail. The algorithm's core functionality includes cyclic frequency detection and spatial spectrum estimation through subspace decomposition techniques.
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