Improving the Estimation Accuracy and Computational Efficiency of ISAR Range Alignment

A well-established approach in inverse synthetic aperture radar (ISAR) translational motion compensation consists of two parts: range alignment and autofocus. Range alignment coarsely estimates the translational motion based on the similarity of the amplitude envelopes of the range-compressed signal. Existing algorithms are becoming performance limited due to the increased spatial resolution of modern state-of-the-art radar systems. To overcome this, we propose new loss functions and a new optimization method for range alignment algorithms based on mathematical optimization. Our method is demonstrated with X-band ISAR data where the range resolution is 10 cm and the object rotates 25 degrees during the coherent processing interval. The proposed loss functions are shown to increase the estimation performance as much as 35 percent and the numerical optimization method reduces the computational cost by an order of magnitude.