3-D Instantaneous Reconstruction of Maneuvering Target Based on Scatterer Information Inversion

3-D images can accurately characterize target structures, with high-quality 2-D inverse synthetic aperture radar (ISAR) images serving as the foundation. However, for maneuvering targets, echo signals exhibit time-varying Doppler characteristics, and the Keystone transform cannot fully compensate for migration through range cells (MTRCs), causing 2-D ISAR image defocusing and degrading 3-D imaging quality. This article proposes an instantaneous 3-D image reconstruction method for maneuvering targets based on scatterer information inversion. First, to address scatterer distinction difficulties in defocused ISAR images, a coarse scatterer classification algorithm based on density-based spatial clustering of applications with noise (DBSCAN) is proposed. Point cluster sets are extracted from defocused images, then classified using DBSCAN clustering. Second, to tackle registration and interferometry challenges for different ISAR images, an approach transforming image-level processing into scatterer-level processing is proposed. Extracted point cluster information inversely derives 1-D high-resolution range profile (HRRP) information of each scatterer at different instants. An improved multiple-hypothesis tracking (MHT) algorithm is used to fine-tune scatterer classification, while phase-derived angle and range measurement techniques reconstruct instantaneous 3-D target images. Finally, to improve imaging quality under low signal-to-noise ratio (SNR) conditions in single-snapshot 3-D reconstruction, a multisnapshot joint processing strategy is proposed. Both simulation and measured data have verified the effectiveness of the proposed method.