MATLAB Code Implementation for ISAR Imaging Processing

Overview of ISAR Imaging Processing Technology ISAR (Inverse Synthetic Aperture Radar) imaging is a radar signal processing technique that generates two-dimensional high-resolution images by analyzing the relative motion between the target and radar. The core principle involves utilizing Doppler frequency shift and range information to convert echo signals into visual images in the spatial domain. Implementing ISAR processing in MATLAB typically includes the following key steps:

Data Preprocessing Raw echo data requires noise reduction, range pulse compression (such as matched filtering), and motion compensation (to eliminate phase errors caused by target translation). Motion compensation represents the most challenging aspect of ISAR imaging, directly impacting the final image quality. In MATLAB implementation, this involves using correlation-based envelope alignment algorithms and phase gradient autofocus (PGA) techniques to correct trajectory errors.

Time-Frequency Analysis Common methods like Short-Time Fourier Transform (STFT) or Wigner-Ville Distribution are employed to extract micro-Doppler characteristics of targets. Time-frequency analysis transforms signals from time domain to frequency domain, providing resolution enhancement for subsequent imaging. MATLAB's spectrogram function or custom STFT implementations using windowed FFT operations are typically utilized here.

Imaging Algorithms Mainstream methods include Range-Doppler (RD) algorithm and Polar Format Algorithm (PFA). The RD algorithm, implemented through two-dimensional FFT operations, is suitable for data after motion compensation. PFA requires interpolation processing and is more appropriate for large rotation angle scenarios. The RD algorithm implementation in MATLAB involves applying fft2 for efficient frequency domain transformation.

Image Post-processing Window functions (such as Hamming window) are applied to suppress sidelobes, while CLEAN algorithms help remove clutter. Final image contrast enhancement is achieved through amplitude normalization and logarithmic scaling. MATLAB's window functions can be directly applied before IFFT operations, and image display optimization uses imagesc with proper axis calibration for actual range/azimuth units.

MATLAB Implementation Key Points Utilize fft/ifft functions for rapid frequency domain transformations. Motion compensation can be achieved through envelope alignment and phase gradient autofocus (PGA) algorithms. Imaging results are displayed using imagesc for 2D images, with axes calibrated to actual range/azimuth units through proper scaling factors.

Extended Applications ISAR imaging can integrate deep learning techniques (such as CNN) for automatic target recognition, or optimize imaging efficiency in sparse scenarios through compressed sensing theory. MATLAB's Deep Learning Toolbox provides frameworks for integrating neural networks with traditional ISAR processing pipelines.