Automated detection and tracking of space debris using Explainable AI

人类在太空的活动和干预，例如报废的卫星、发射的火箭，导致大量碎片在太空中积聚。据报道，空间中存在3万多块10厘米以上的空间碎片和1亿多块1毫米以上的空间碎片。此外，一些自然空间环境组成部分（例如轨道上的流星体）导致污染水平迅速上升，反过来又扰乱了可持续的空间业务。总的来说，这种污染对现役遥感卫星的预期/正常运行造成了很大的干扰。影响：强有力的碎片规避演习在减少卫星之间、卫星之间以及卫星与闲置物体之间相互勾结的风险方面发挥着至关重要的作用。差距：即使对地面设施来说，数据收集以及准确探测和跟踪碎片轨迹既昂贵又困难；在有限的监控设施空间内实现这一目标更具挑战性。我们的第一个目标是通过建立一个大型图像库，将无碎片空间环境图像与包含碎片的空间环境图像区分开来，并对碎片类型进行准确分类。在此步骤之后，将开发用于跟踪多类碎片的高级算法，例如神经网络辅助KalmanNet。这包括克服遮挡和背景杂乱的挑战。目标：(i)管理一个大型空间环境图像库，包括不同类别的空间碎片；（ii）开发使用深度神经网络的碎片检测模型；（ii）实施针对多类碎片的跟踪算法，克服遮挡和背景杂波的挑战。（vi）探索可解释的人工智能技术，为深度学习模型添加可解释的功能，使决策对用户透明。预测和跟踪算法必须在推理时间内快速执行以产生准确的结果。提高实时碎片跟踪模型的跟踪速度尤为重要。

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.