On the compression of spacecraft housekeeping data using discrete cosine transforms

To gain deeper insight into the behavior the mechanical and thermal stress and the environment of a spacecraft extended monitoring is required. This implies the application of high resolution sensors in both value and time domain which produces a considerable amount of data. Thus in order to optimize the information throughput of the bandwidth available for monitoring we propose a compression algorithm derived from well-established approaches for image compression. Namely a two-dimensional discrete cosine transform (DCT) will be applied to compress one-dimensional time-series data of various sensor types. Data quality in terms of mean square error and peak signal-to-noise ratio as well as compression ratio of the introduced algorithm together with its derived pareto optimal set of parameters will be verified by applying it to real-world sets of housekeeping data. We will use different sensor samples of both satellites (AISat) and launchers (ARIANE 5).

I. Introduction

Precise determination of spacecraft performances and environments requires sampling of on-board sensors during actual missions [1]. Analysis of on-board sensor data, provided via telemetry, allows to reduce uncertainties in design parameters, resulting in residual improvements, with the goal of minimizing costs for future missions. Growing numbers of sensors and increasing sensor cadences require more on-board lossless data compression [2] and lossy data compaction [3] to provide the maximum amount of information over the bandwidth limited telemetry downlink. The incurring computational effort is excessive for the radiation hardened on-board computers (OBCs) in current use [4]. Driven by the growing demand for on-board computing power, efforts to introduce commercial off the shelf (COTS) products in space [5] result in additional computing power available [6] to close this gap.