ISAR Imaging of Targets With Complex Motions Based on a Noise-Resistant Parameter Estimation Algorithm Without Nonuniform Axis

In inverse synthetic aperture radar (ISAR) imaging of targets with complex motions, such as highly maneuvering airplanes and ships fluctuating with oceanic waves, azimuth echoes of a range cell have to be modeled as multicomponent cubic phase signals (CPSs) after the range alignment and the phase adjustment. Due to the time-varying Doppler frequencies of scatterers, ISAR image obtained with the standard range-Doppler algorithm is blurred, and the range-instantaneous-Doppler (RID) technique is required to improve the image quality. In this paper, by employing a novel parametric autocorrelation function and the generalized scaled Fourier transform, an effective parameter estimation algorithm is proposed for multicomponent CPSs and applied to reconstruct the RID image for targets with complex motions. Analyses of the implementation, the cross-term, the anti-noise performance, and the computational cost demonstrate that, compared with other three representative estimation algorithms, the proposed algorithm can eliminate the brute-force searching procedure and acquire a higher anti-noise performance without the nonuniform axis. Through simulations and analyses for synthetic models and the real radar data, we verify the effectiveness of the proposed estimation algorithm and the corresponding ISAR imaging algorithm.