Continuous phase modulation recognition algorithm based on fuzzy entropy
To address the recognition challenge of Multi-h Continuous Phase Modulation(Multi-h CPM)signals with varying modulation parameters,this paper proposes a modulation recognition algorithm grounded in fuzzy entropy theory.This theory transcends the binary approach of distance and count-based similarity in approximate entropy,opting for a membership function to assess similarity and more accurately reflect the complexity of time series.The algorithm separates and calculates the fuzzy entropy of the in-phase and quadrature components of the received signal,utilizing these values as classification features for a Support Vector Machine(SVM).Experiments demonstrate that the algorithm achieves 100%recognition accuracy for full-response rectangular shaped Multi-h CPM signals across various modulation index sets at signal-to-noise ratios above 6 dB,and enables modulation recognition with a minimal number of symbols.
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