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

TL;DR

This paper presents a method for recognizing and tracking external features of resident space objects in ISAR images using feature detection, Hough transform, and sequence analysis to improve space domain awareness.

Contribution

It introduces a novel approach combining feature detection, tracking, and sequence analysis in ISAR imagery for RSO recognition, enhancing detection confidence and classification accuracy.

Findings

Feature tracking improves detection confidence.

Hough transform accurately detects linear features.

Sequence analysis aids in feature classification.

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.