Adaptive Compression Schemes for Housekeeping Data

Abstract

Extended monitoring of housekeeping data is required to increase the observability of a spacecrafts health status, its environment and resulting mechanical stress as well as physical parameters like the spacecrafts position and orientation. This implies the application of an increasing number of onboard sensors for various physical quantities like temperature, vibration, acceleration, voltage, current and others. These sensors need to offer high resolution in the time domain and high accuracy. The amount of data produced by an extended housekeeping system proves increasingly significant. However, to customers, housekeeping data is not of direct value and has therefore been subordinated to scientific payload data in terms of the allocation of bandwidth towards ground. In order to optimize the information throughput for a given bandwidth budget, data compression such as entropy coding as well as lossy data compaction need to be applied. At the same time, the accuracy and the allowed magnitude of error of housekeeping data is crucial to its value for ground engineers. As a result, especially lossy data compaction has to be applied carefully taking into account the nature of the data to be processed. In this paper, we evaluate transform-based compression techniques and analyze their effect on housekeeping data and suitability for subsequent entropy coding on board spacecrafts. To do so, we apply a variety of transforms to real sensor data collected by launchers (ARIANE5) as well as satellites (AISat) and analyze their performance in terms of data quality, compression ratio, computing effciency and effectiveness of subsequent entropy coding. Our results show that a data reduction of 96.5% for quickly oscilatting vibration sensors and of 99.5% for slower temperature sensors can be achieved without introducing a significant error during critical time frames within data sequences.