To improve the accuracy and robustness of the Multi-Function Radar (MFR) waveform unit identification

a waveform unit identification method combining Stacked Denoising Autoencoders (SDAE) with Support Vector Machine (SVM) is proposed. The traditional MFR signal processing method relying on pulse sequence analysis is abandoned. A MFR waveform unit segmental identification model is proposed by analyzing the structure of waveform unit and the union variation characteristics of parameters. By using this model

the traditional identification of pulse sequences can be converted to the identification of MFR waveform unit. On the basis of this model

SDAE algorithm is introduced. The training sample data and SDAE hidden layer nodes are processed by noise adding. Using the training sample data

the SDAE network model is trained

and the deep robust feature of the sample data is extracted. Lastly

SVM algorithm is introduced. By using the deep feature of the SDAE output

the SVM model is optimized

and the final waveform unit identification model can be obtained. Simulation results show that under the same condition of sample number and test error

the proposed method can achieve high recognition accuracy

and has better identification results than SVM algorithm. The MFR waveform unit segmental identification model is verified. Besides

through the introduction of SDAE

the proposed SDAE-SVM method can autonomously dig up the deep feature of original data

and improves the robustness and accuracy of the waveform unit identification.