Autofocus algorithm for radar/sonar imaging by exploiting the continuity structure

In this paper, a sparsity-driven auto-focus technique is developed for radar/sonar imaging by exploiting the continuity structure of the target scene under Bayesian framework. After range compression, structured sparse prior is imposed in a statistical manner on each range cell to encourage the continuities in cross-range domain by clustering the scatterers with nonzero magnitudes. Based on a statistical framework, the proposed algorithm can simultaneously cope with structured sparse recovery and phase error correction problem. Focused high-resolution radar image can be obtained by iteratively estimating scattering coefficients and phase error. Compared to previous sparsity-driven auto-focus approaches, the proposed algorithm can desirably preserve the target region, alleviate over-shrinkage problem and consequently yield more accurate phase error estimate due to the structured sparse constraint. The simulation results demonstrate that the proposed algorithm can obtain more concentrated images within a small number of iterations, particularly in low SNR and heavily smeared scenarios.