Inverse Synthetic Aperture Radar (ISAR) is a crucial radar imaging technique that relies on the relative motion between the radar and the target to produce high-resolution images. However, traditional ISAR methods are highly sensitive to inaccuracies in estimating rotational parameters, such as roll, pitch, and yaw. Errors in these estimates can result in significant image degradation. In this paper, we present a novel approach for imaging dynamically rotating scenes using a multistatic ISAR setup, where we estimate motion parameters through a Differential Semblance Optimisation (DSO) criterion. This is done by minimising discrepancies between images formed from multiple transmitter-receiver pairs, our method delivers focused images with improved estimates of the yaw rotation parameter of the targets. We demonstrate the effectiveness of this approach through a series of numerical experiments, highlighting its robustness in both noise-free and noisy environments, and discuss its potential for enhancing ISAR imaging in complex scenarios.

The motion of targets is well known to result in their defocussing and displacement in SAR imagery, but detection of motion and re-focussing of targets under generic conditions remains of ongoing interest. One class of methods involves forming images of sub-apertures in which motion defocussing will be reduced. In this paper, we use dynamic tomographic image formation methods utilising an optical flow constraint to form a video of SAR sub-aperture images. These retain fine resolution of the full aperture, focussing along-track motion.