An ISAR target motion estimation algorithm based on a differential semblance criterion

Inverse Synthetic Aperture Radar (ISAR) is a vital radar imaging technique that leverages the relative motion between the radar and the target to generate high-resolution images. Traditional ISAR methods; however, are highly sensitive to inaccuracies in estimating rotational parameters, roll, pitch, and yaw, leading to image degradation. This article proposes a novel Differential Semblance Optimization (DSO) criterion for imaging dynamically rotating targets in a multistatic ISAR configuration. Unlike the Intensity Criterion (IC), which requires a precise initial parameter range, DSO enables broader exploration of value ranges, offering greater flexibility. Although the experiments focus on yaw rotation, the method is versatile and extendable to other rotational parameters. Tests with varying transmitter and receiver configurations demonstrate that DSO maintains robust performance even with fewer receivers. Comparisons with IC show that DSO produces sharper, more focused images and performs robustly in noisy environments, underscoring its potential for enhancing ISAR imaging in complex and dynamic scenarios.