In this paper, models of jamming signals are established based on the mechanism of active jamming signals against LFM radar. Five time-domain characteristics and frequency-domain characteristics of jamming signals are extracted. The decision tree method, BP neural network method and decision tree support vector machine (DTSVM) method are used to establish the classification models, and the simulation is performed for identifying and classifying the jamming signals at different jamming-to-noise ratio (JNR). The result shows that the model based on DTSVM method has better adaptability, smaller calculation and higher recognition success rate at low JNR.
In the process of extracting rotor features using time-frequency analysis, clutter suppression and improving time-frequency resolution have always been problems that need to be solved and improved. The paper proposes a rotor feature extraction method with high time-frequency resolution that can suppress clutter. Firstly, the separation of the micro-motion target and the clutter is realized by the complex empirical mode decomposition (CEMD). The high-resolution time-frequency diagram of the rotor is obtained by the synchrosqueezing improved S transform (SIST) proposed in the paper. The features extracted from the diagram are of high accuracy. The simulation results show that this method (CEMD-SIST) has better clutter suppression performance and higher time-frequency resolution than other rotor feature extraction methods.