Automated detection and tracking of space debris using Explainable AI

Human activities and interventions in space e.g. dead satellites, launched rockets lead to accumulation of a large amount of debris in the space. It is reported that more than 30,000 of pieces of space debris that are larger than 10 cm and more than 100 million pieces of debris larger than 1 mm exist in the space. In addition to that, some natural space environment components (e.g. meteoroids in orbits) lead to a rapid rise in the level of pollution and in return perturb the sustainable space operations. Collectively, this pollution leads to a great interference to the expected/normal operation of currently active remote sensing satellites. Impact: Robust debris avoidance manoeuvres play a vital role in reducing the risk of collusion between satellites, with each other and also between satellites and idle objects. Gap: Data collection as well as detection and tracking of the trajectories of debris accurately is costly and difficult, even for ground-based facilities; which is even more challenging to achieve it within the limited space surveillance facilities.

Our first aim is to distinguish the debris-free space environment images from the ones including debris and classifying the type of debris accurately by curating a large-scale image repository. Following this step, advanced algorithms e.g. NN-aided KalmanNet, for tracking multiple classes of debris will be developed. This involves overcoming the challenge of occlusion and background clutter.

Objectives:
(i) To curate a large image repository of space environment including different categories of space debris; (ii) to develop debris detection models using deep neural networks, (ii) to implement tracking algorithms for multiple classes of debris that overcome the challenge of occlusion and background clutter. (vi) To explore explainable AI techniques to add explainable functionality to the deep learning models so that the decision is transparent to the users. Prediction and tracking algorithms must perform quickly during the inference time to yield accurate results. Enhancing tracking speed is especially imperative for real-time debris tracking models.