基于改进组合机器学习的卫星遥测参数预测

Satellite Telemetry Parameter Prediction Based on Improved Combinatorial Machine Learning

对在轨卫星的运行状态进行监测、分析以及异常检测是卫星在轨运行管理的重要内容。预测卫星遥测参数序列的变化趋势,对卫星异常检测与处置、保障安全运行非常必要。针对目前对于周期性不明显且具有多种变化特征的遥测参数预测精确度不够的问题,本文引入对遥测参数的预测有辅助作用的因素作为协变量,提出了基于改进组合机器学习的预测模型。该模型使用全局模型和局部模型分别获取遥测参数序列的趋势特征和局部不规则波动特征,并采用改进的注意力机制捕获多维参数之间的关联关系,提高了预测精度。此模型可以提供点预测和区间预测的结果,为在轨卫星处置决策提供了更多输入。在科学卫星真实遥测数据集和时间序列公开数据集上验证了本文方法的有效性。

The monitoring,analysis and anomaly detection of the operational status of satellites in orbit are important contents of satellite operational management.It is very necessary to predict the changing trend of satellite telemetry parameter data series for detecting,dealing with satellite anomalies and ensuring the safe operation of satellites.Aiming at the problem that the current prediction research is not accurate enough for telemetry parameters with insignificant periodicity and multiple changing characteristics,this paper introduces covariates that are helpful for telemetry parameter sequence prediction,and proposes a prediction model based on improved combined machine learning,using the global model and the local model to obtain the trend characteristics and local irregular fluctuation characteristics of the telemetry parameter sequence respectively,and the improved Attention mechanism is used to capture the correlation between multi-dimensional parameters to improve the prediction accuracy.At the same time,this model can provide point prediction and interval prediction results for the telemetry data sequence,providing more input for the decision-making of on-orbit satellite disposal.The validity of the proposed method is verified on the real telemetry data set of scientific satellites and the public time series data sets.