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首页> 外文会议>Symposium on Space Operations; 20060619-23; Rome(IT) >Perspective on Deep Space Network System Performance Analysis
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Perspective on Deep Space Network System Performance Analysis

机译：深度空间网络系统性能分析透视

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This chapter presents the context of ongoing performance analysis activity that is taking place in the Deep Space Network. This activity includes root-cause analysis of failures to enable effective solutions. It also includes the performance assessment, aiming to establish how well the equipment is meeting its specifications and how much margin is available. The assessment establishes a baseline performance on data-return metrics and specific performance-related parameters that affect the quality of services provided. These include antenna pointing, system noise temperature, Doppler noise, frequency and amplitude stability, wide-area-network bandwidth loading, offset in frequency and time references, and the track setup time. In general, the system exhibits good performance and meets the specifications. The averaged statistics on data QQCL, bandwidth loading, frequency and timing offsets, and tracking setup time show a reasonable margin. An area of concern is antenna pointing, particularly at Ka-band. More effort will be required to achieve a consistent performance as specified.The performance analysis activity is still in the early stage of development. Additional effort is needed to improve the modeling to properly account for the observations. There remain many unexplored metrics. Thus, this work continues to be active and exciting in the near future. Looking back, some of the lessons learned are the following:1) Accurate accounting of data return relative to the expectation is a challenge. More work is needed to establish the expected data return at the level of telemetry frames or transmitted command units. The information available in today's system, which is time based, does not present itself readily for this analysis.2) Correct interpretation of the observed performance is crucial. Often, the specifications are for the ground system. The observation data collected from tracking spacecraft, however, include other non-ground effects, such as space media and the spacecraft. One has to be careful with the comparison. To the extent possible, one needs to create a model that accounts for all effects.3) Special care is required in data processing. Rather than blindly processing the measurement generated by the system, one needs to verify that the generated data are valid. For example, in the case of system noise temperature analysis, the measurement accuracy is dependent on the SNR condition. A too-strong received signal may corrupt noise temperature measurements; thus, data would need to be excluded.
机译：本章介绍了深层空间网络中正在进行的性能分析活动的背景。此活动包括对故障的根本原因进行分析以实现有效的解决方案。它还包括性能评估，旨在确定设备满足其规格的程度以及可用的余量。该评估会根据数据返回指标和会影响所提供服务质量的与性能相关的特定参数建立基准性能。其中包括天线指向，系统噪声温度，多普勒噪声，频率和幅度稳定性，广域网带宽负载，频率和时间基准的偏移以及跟踪建立时间。通常，该系统表现出良好的性能并符合规格。数据QQCL，带宽负载，频率和时序偏移以及跟踪建立时间的平均统计数据显示出合理的余量。关注的领域是天线指向，特别是在Ka频段。要达到指定的一致性能，将需要付出更多的努力。性能分析活动仍处于开发的早期阶段。需要付出额外的努力来改进建模，以正确说明观察结果。仍有许多未开发的指标。因此，在不久的将来这项工作将继续活跃而令人兴奋。回顾过去，可以汲取以下一些经验教训：1）相对于期望准确地计算数据返回是一个挑战。需要更多的工作来建立遥测帧或发送的命令单元级别的预期数据返回。当今系统中基于时间的信息无法进行分析。2）正确解释观察到的性能至关重要。通常，规格是针对地面系统的。但是，从跟踪航天器收集的观测数据包括其他非地面效应，例如空间媒体和航天器。必须谨慎进行比较。必须尽可能创建一个模型，说明所有影响。3）在数据处理中需要特别注意。与其盲目处理系统生成的测量值，不如验证生成的数据是否有效。例如，在系统噪声温度分析的情况下，测量精度取决于SNR条件。接收信号过强可能会破坏噪声温度测量；因此，将需要排除数据。

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