ISAR imaging of uniformly rotating targets via parametric weighted L1 minimization

The ISAR imaging problem can be converted to a sparse recovery problem and efficiently solved by a weighted L₁ minimization algorithm, which outperforms the regular L₁ minimization substantially, provided the sparse representation is already known. However, the sparse representation of the ISAR signal is related to the target rotation rate, which is usually unknown for noncooperative target. In this paper, we propose a computationally efficient parametric weighted L₁ minimization algorithm to retrieve both the sparse representation and the ISAR image. The proposed algorithm can adaptively update the rotation rate estimation to the true value after several iterations. Numerical experiments show that the approach is efficient in estimating the target rotation rate and recovering the high resolution ISAR image.