Persistent feature reconstruction of resident space objects (RSOs) within inverse synthetic aperture radar (ISAR) images

TL;DR

<3-5 sentence high-level summary> The paper addresses space-domain awareness of resident space objects using high-resolution sub-THz ISAR imagery from a space-based platform. It proposes an unsupervised processing pipeline that detects and tracks linear features across ISAR frame sequences using a double-weighted Hough transform, affine alignment, and DBSCAN clustering. A cumulative image approach reduces frame-specific artefacts and reveals persistent structural features, with a shadow-detection use case illustrating the capability to distinguish occlusions from true features. The results suggest that persistent feature reconstruction increases detection confidence and supports robust RSO characterization for SDA decisions.

Abstract

With the rapidly growing population of resident space objects (RSOs) in the near-Earth space environment, detailed information about their condition and capabilities is needed to provide Space Domain Awareness (SDA). Space-based sensing will enable inspection of RSOs at shorter ranges, independent of atmospheric effects, and from all aspects. The use of a sub-THz inverse synthetic aperture radar (ISAR) imaging and sensing system for SDA has been proposed in previous work, demonstrating the achievement of sub-cm image resolution at ranges of up to 100 km. This work focuses on recognition of external structures by use of sequential feature detection and tracking throughout the aligned ISAR images of the satellites. The Hough transform is employed to detect linear features, which are tracked throughout the sequence. ISAR imagery is generated via a metaheuristic simulator capable of modelling encounters for a variety of deployment scenarios. Initial frame-to-frame alignment is achieved through a series of affine transformations to facilitate later association between image features. A gradient-by-ratio method is used for edge detection within individual ISAR images, and edge magnitude and direction are subsequently used to inform a double-weighted Hough transform to detect features with high accuracy. Feature evolution during sequences of frames is analysed. It is shown that the use of feature tracking within sequences with the proposed approach will increase confidence in feature detection and classification, and an example use-case of robust detection of shadowing as a feature is presented.

Figures (9)

• Figure 1: Subset of simulated ISAR images of Skynet 5D at 300 GHz with 5 GHz bandwidth. Each frame has been processed with four-times zero padding and Hanning windowing. Row a) shows unaligned frames, and b) shows their aligned counterparts. The grazing angles relative to the plane of the solar panels is noted.
• Figure 2: (a) CAD model and (b) ISAR image annotated with examples of potentially identifiable features. In the ISAR image, imaging artefacts (shadowing and range extended returns) are also annotated. The radar imaging plane does not directly correlate to the orientation of the CAD model, which has been chosen to make feature correspondence between CAD and ISAR easily understood.
• Figure 3: Images showing a) gradient magnitude, b) gradient direction, and c) binary mask of significant regions.
• Figure 4: Comparison of (a) standard Hough transform implementation, and (b) the double-weighted Hough transform. In additional to the overall reduction in interference noise, key differences have been annotated: A. a true peak is indistinguishable from noise in (a), but clearly detectable in (b); B. retention of direction information produces unique results across the entire $\theta$ range, rather than duplicated values.
• Figure 5: DBSCAN clustering results, visualised in expanded-3D parameter space. Each point is colour-coded according to its cluster, with unclustered points in black