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Different Long Short-Term Memory Approaches to Enhance Prediction-Based Satellite Telemetry Compression
Journal of Aerospace Information Systems ( IF 1.5 ) Pub Date : 2020-12-29 , DOI: 10.2514/1.i010906
Tarek A. Mahmoud 1 , Ahmed F. Shehab 1 , Mohamed A. Elshafey 1
Affiliation  

Motivated by the success of deep learning in recent years, prediction-based methods are used to compress satellite telemetry data. In this paper, two-stage lossless compression methods for telemetry data are demonstrated. In the first stage, different approaches of long short-term memory (LSTM) based on one-to-one, many-to-one, and many-to-many network architectures are presented. The framework of implementing each approach, as a predictor, is discussed. In the second stage, a set of competing entropy coding methods are tested and evaluated. The presented approaches are capable of exploring correlation dependencies between consecutive samples in the individual and/or successive telemetry frames. The proposed approaches are introduced in two different versions: stacked- and nonstacked-based LSTM architectures trying to achieve higher prediction efficiency. The proposed approaches are tested on real telemetry data, in frames of different data word widths, in distinct FUNcube satellite sessions. The performance of each presented approach, as a predictor, is evaluated based on prediction gain, and reduction in entropy. However, the performance of the whole two-stage lossless compression method is assessed by compression ratio. Comparative analysis is preformed among the proposed approaches, and the improvements are verified against the state-of-the-art prediction-based approaches.



中文翻译:

不同的长期短期记忆方法来增强基于预测的卫星遥测压缩

由于近年来深度学习的成功,基于预测的方法被用于压缩卫星遥测数据。本文介绍了遥测数据的两阶段无损压缩方法。在第一阶段,提出了基于一对一,多对一和多对多网络体系结构的长短期记忆(LSTM)的不同方法。讨论了将每种方法用作预测器的框架。在第二阶段,测试和评估了一组竞争性熵编码方法。所提出的方法能够探索各个和/或连续遥测帧中的连续样本之间的相关性依赖性。提议的方法有两种不同的版本:基于堆叠和非堆叠的LSTM架构试图实现更高的预测效率。在不同的FUNcube卫星会话中,在不同数据字宽的帧中对真实遥测数据进行了测试。基于预测增益和熵的降低,评估每种提出的方​​法(作为预测器)的性能。但是,整个两阶段无损压缩方法的性能是通过压缩率来评估的。在提议的方法之间进行了比较分析,并针对基于最新预测的方法验证了改进。基于预测增益和熵的减少来评估。但是,整个两阶段无损压缩方法的性能是通过压缩率来评估的。在提议的方法之间进行了比较分析,并针对基于最新预测的方法验证了改进。基于预测增益和熵的减少来评估。但是,整个两阶段无损压缩方法的性能是通过压缩率来评估的。在提议的方法之间进行了比较分析,并针对基于最新预测的方法验证了改进。

更新日期:2020-12-30
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