A fast eigenvector-based autofocus method for sparse aperture ISAR sensors imaging of moving target

Motion compensation is of significant importance in generating high-focused inverse synthetic aperture radar (ISAR) images. The range misalignment and the phase error of each received echo are two important parameters in ISAR imaging and they need to be precisely estimated and compensated. Faced with sparse aperture (SA) data, we use the minimum entropy-based method for range alignment and propose a fast eigenvector-based autofocus method for phase adjustment in this paper. The novel autofocus method uses the Lanczos or Arnoldi iteration to compute only the desired eigenvector instead of directly doing time-consuming eigen-decomposition to the sample covariance matrix. Therefore, the computational complexity can be greatly reduced. Simulated scatterer model and raw data are utilized to prove that the proposal is an efficient autofocus algorithm with a lower computational burden.