Abstract

For targets with complex motions, such as highly maneuvering airplanes and ships fluctuating with oceanic waves, the Doppler frequencies of scatterers are actually time-varying and azimuth echoes of a range cell have to be modeled as multicomponent cubic phase signals (CPSs) after the range alignment and the phase adjustment. In inverse synthetic aperture radar (ISAR) imaging based on the CPS model, the chirp rate and the quadratic chirp rate are identified as causes of the image defocus. In this paper, by employing a novel parametric symmetric self-correlation function and the keystone transform, an effective estimation algorithm, known as the modified Lv’s distribution (MLVD), is presented for the CPS and applied to ISAR imaging of targets with complex motions. The MLVD is simple and can be easily implemented using the complex multiplication, the fast-Fourier transform (FFT), and the inverse FFT (IFFT). The implementation, the cross-term, the antinoise performance, and the computational cost are analyzed for the MLVD. Compared to three representative estimation algorithms for the CPS, the MLVD can acquire a higher antinoise performance and eliminate the brute-force searching without the interpolation. Through simulations and analyses for synthetic models and the real radar data, we verify the effectiveness of the MLVD and the corresponding ISAR imaging algorithm.

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