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For inverse synthetic aperture radar (ISAR) imaging of targets with nonsevere maneuverability, the Doppler frequencies of scatterers are actually time-varying and azimuth echoes of a range cell have to be modeled as multicomponent linear frequency modulation (LFM) signals after the range alignment and the phase adjustment. In ISAR imaging with the LFM signal model, the chirp rate deteriorates the target image and an effective parameter estimation algorithm is required. By employing a symmetric instantaneous self-correlation function and the modified scaled Fourier transform, an effective parameter estimation algorithm, known as the centroid frequency chirp rate distribution (CFCRD), is proposed and applied to ISAR imaging. Compared to two representative parameter estimation algorithms, the modified Wigner-Ville distribution and the Lv’s distribution, the proposed CFCRD can acquire a higher antinoise performance without spectrum aliasing and brute-force searching. Through simulations and analyses of the synthetic radar data and the real radar data, we verify the effectiveness of CFCRD and the corresponding ISAR imaging algorithm.

Inverse synthetic aperture radar imaging of targets with nonsevere maneuverability based on the centroid frequency chirp rate distribution