Parameter estimation of Fh data link signal based on time-frequency ridge

Jinghui Li; Fan Zhou

doi:10.1117/12.2675346

25 May 2023 Parameter estimation of Fh data link signal based on time-frequency ridge

Jinghui Li, Fan Zhou

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126360S (2023) https://doi.org/10.1117/12.2675346
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

To solve the problem of low estimation accuracy of FH data link signal parameters based on traditional time-frequency ridge method under low SNR, a time-frequency clustering estimation method based on GA optimization is proposed. Firstly, genetic algorithm is used to extract the time-frequency interval of STFT time-frequency diagram of data link signal; then the time-frequency ridge is extracted; finally, K-means clustering algorithm is used to estimate the frequency hopping frequency by classifying the time-frequency ridge with a cluster number of 6. Experimental results show that this method can accurately estimate the frequency hopping frequency under SNR =−18dB, and effectively improve the accuracy of parameter estimation under low signal-to-noise ratio compared with the traditional time-frequency ridge parameter estimation method.

Citation Download Citation

Jinghui Li and Fan Zhou "Parameter estimation of Fh data link signal based on time-frequency ridge", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126360S (25 May 2023); https://doi.org/10.1117/12.2675346

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