We apply deep learning techniques for anomaly detection to analyze activity of Russian-owned resident space objects (RSO) prior to the Ukraine invasion and assess the results for any findings that can be used as indications and warnings (I&W) of aggressive military behavior for future conflicts. Through analysis of anomalous activity, an understanding of possible tactics and procedures can be established to assess the existence of statistically significant changes in Russian RSO pattern of life/pattern of behavior (PoL/PoB) using publicly available two-line element (TLE) data. This research looks at statistical and deep learning approaches to assess anomalous activity. The deep learning methods assessed are isolation forest (IF), traditional autoencoder (AE), variational autoencoder (VAE), Kolmogorov Arnold Network (KAN), and a novel anchor-loss based autoencoder (Anchor AE). Each model is used to establish a baseline of on-orbit activity based on a five-year data sample. The primary investigation period focuses on the six months leading up to the invasion date of February 24, 2022. Additional analysis looks at RSO activity during an active combat period by sampling TLE data after the invasion date. The deep learning autoencoder models identify anomalies based on reconstruction errors that surpass a threshold sigma. To capture the nuance and unique characteristics of each RSO an individual model was trained for each observed space object. The research made an effort to prioritize explainability and interpretability of the model results thus each observation was assessed for anomalous behavior of the individual six orbital elements versus analyzing the input data as a single monolithic observation. The results demonstrate not only statistically significant anomalies of Russian RSO activity but also details anomalous findings to the individual orbital element.

翻译：本研究应用深度学习技术进行异常检测，分析乌克兰入侵前俄罗斯所属在轨空间物体（RSO）的活动模式，并评估其结果能否作为未来冲突中侵略性军事行为的预警指标。通过对异常活动的分析，我们利用公开的两行轨道根数（TLE）数据，建立对潜在战术与程序的理解，以评估俄罗斯RSO在轨行为模式是否存在统计显著的变化。本研究探讨了统计方法与深度学习在异常活动评估中的应用。评估的深度学习方法包括孤立森林（IF）、传统自编码器（AE）、变分自编码器（VAE）、Kolmogorov Arnold网络（KAN）以及一种新颖的基于锚点损失的自编码器（Anchor AE）。每种模型均基于五年数据样本建立在轨活动的基线。主要调查时段聚焦于2022年2月24日入侵日期前的六个月。额外分析通过采样入侵日期后的TLE数据，考察了实战期间的RSO活动。深度学习自编码器模型通过识别超过阈值sigma的重构误差来检测异常。为捕捉每个RSO的细微特征与独特性，本研究为每个观测到的空间物体单独训练模型。研究特别注重模型结果的可解释性，因此每个观测值均针对六个独立轨道根数的异常行为进行评估，而非将输入数据作为单一整体进行分析。结果表明，不仅俄罗斯RSO活动存在统计显著的异常，而且异常发现可具体定位至单个轨道根数。