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Abstract <p>This study presents a method for detecting anomalous measurements in the trajectory data of spacecraft, based on recursive partitioning of the time series of observations. This method analyzes the standard deviation of the data, effectively identifying anomalous measurements characterized by elevated noise levels. A significant advantage of this approach is the lack of requirement for prior knowledge of the initial orbital approximation and the absence of a need for pre-training. It has been tested on synthetic datasets with artificially introduced anomalies, as well as on real data from the <i>Spektr</i>-<i>R</i> spacecraft. The results demonstrated an accuracy of 96% compared to other traditional anomaly-detection methods. The algorithm of this method is applicable to various types of orbits and scales of observations. Its code is available for public use.</p>

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Recursive Partitioning for Anomaly Detection in Tracking Satellite Data

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • P. R. Zapevalin

摘要

Abstract

This study presents a method for detecting anomalous measurements in the trajectory data of spacecraft, based on recursive partitioning of the time series of observations. This method analyzes the standard deviation of the data, effectively identifying anomalous measurements characterized by elevated noise levels. A significant advantage of this approach is the lack of requirement for prior knowledge of the initial orbital approximation and the absence of a need for pre-training. It has been tested on synthetic datasets with artificially introduced anomalies, as well as on real data from the Spektr-R spacecraft. The results demonstrated an accuracy of 96% compared to other traditional anomaly-detection methods. The algorithm of this method is applicable to various types of orbits and scales of observations. Its code is available for public use.