ISAR Echo Processing Workflow Based on Time-Frequency Analysis

This project utilizes a MATLAB package for ISAR echo processing workflow analysis through time-frequency analysis techniques applied to multi-component linear frequency modulated signals. Time-frequency analysis serves as a powerful tool that simultaneously examines signal characteristics in both frequency and time domains, implemented using functions like spectrogram or wavelet transforms in MATLAB. By employing time-frequency analysis, we can better understand signal variations and extract discriminative features through algorithms such as short-time Fourier transform or Wigner-Ville distribution. When applied to ISAR echo processing workflows, this technique enables more accurate and detailed results by tracking Doppler frequency variations and target motion characteristics. For multi-component LFM signal processing, time-frequency analysis proves particularly effective in separating and analyzing inter-component relationships. The implementation typically involves signal decomposition methods and component extraction algorithms to isolate useful information from overlapping signals. Key MATLAB functions may include signal preprocessing, windowing techniques, and advanced time-frequency representation calculations. In this project, we leverage the advantages of time-frequency analysis to process multi-component LFM signals comprehensively, employing code implementations that feature adaptive parameter selection, noise reduction processing, and component visualization capabilities for thorough signal characterization.