Subspace-based super-resolution algorithm for ground moving target imaging and motion parameter estimation

For synthetic aperture radar (SAR), most conventional algorithms provide a good performance in ground moving target imaging based on Fourier transform (FT). However, when multiple moving targets with the close centres exist, the conventional algorithms may suffer from the performance degradation since the spectra resolution of FT may be limited by the time samples. To address this issue, a super-resolution motion parameter estimation algorithm is proposed in this study. First, Keystone transform is applied to correct the linear range walk. Then the range curvature is compensated by the matched function with respect to the platform velocity. After performing the compensation of linear range walk and range curvature, the energy of a ground moving target is focused on one range cell, and then a first-order discrete polynomial-phase transform is applied to transform the quadratic phase signal into a single tone. After applying the smoothing technique to construct the covariance matrix of full rank, the multiple signal classification algorithm is utilised to estimate the target cross- and along-track velocities, which can significantly improve the motion parameter resolution performance compared with the FFT-based algorithms. The real SAR data processing results are used to validate the effectiveness and feasibility of the proposed algorithm.