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- W2912496696 abstract "Considering that classification of Linear Frequency Modulation-coding(LFM-coding) signals performs bad in complex electromagnetic environment, a methodology based on time-frequency fractal feature analysis and spatial data mining is presented. The method calculates the ambiguity function (AF) of radar signals, and extracts the rotation angles and fractal dimensions of AF main ridges as the feature parameters used to build feature vectors. Then on the basis of spatial data mining, nuclear field is employed to dig out the distribution characteristics of parameter samples in feature space and all samples are dynamically classified to different clusters under the guidance of field strength. Thus, the classification of LFM-coding signals is accomplished. Simulation results indicate that the extracted feature parameters have good stability and separability, and the average correct classification rate of LFM-coding signals can exceed 90% significantly in case of low SNR. The proposed method is proved to be effective when classifying LFM-coding radar signals in complex electromagnetic environment." @default.
- W2912496696 created "2019-02-21" @default.
- W2912496696 creator A5046552287 @default.
- W2912496696 date "2018-12-01" @default.
- W2912496696 modified "2023-09-23" @default.
- W2912496696 title "A Novel Method for LFM-Coding Radar Signals Classification" @default.
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- W2912496696 doi "https://doi.org/10.1109/isape.2018.8634349" @default.
- W2912496696 hasPublicationYear "2018" @default.
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