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Aiming at the problem that the traditional feature parameters are difficult to meet the fastness and effectiveness of frequency-hopping signal sorting recognition, a feature extraction method based on ambiguity function theory is proposed. The algorithm applies the ambiguity function theory to the frequency hopping signal sorting and identification. Firstly, the ambiguity function of the single-hop signal is calculated, then the AFMR slice of the signal is extracted by the PSO algorithm, and the global features of the AFMR slice are extracted to form the feature vector. The results of FCM clustering experiments show that the extracted feature parameters have higher sorting accuracy and can adapt to the larger dynamic signal-to-noise ratio.

Frequency Hopping Signal Sorting Algorithm Based on Global Feature of Ambiguity Function