Space instrumentation

Abstract:

The first of the two missions foreseen in the ExoMars program was successfully launched on 14th March 2016. It included the Trace Gas Orbiter and the Schiaparelli Entry descent and landing Demonstrator Module. Schiaparelli hosted the DREAMS instrument suite that was the only scientific payload designed to operate after the touchdown. DREAMS is a meteorological station with the capability of measuring the electric properties of the Martian atmosphere. It was a completely autonomous instrument, relying on its internal battery for the power supply. Even with low resources (mass, energy), DREAMS would be able to perform novel measurements on Mars (atmospheric electric field) and further our understanding of the Martian environment, including the dust cycle. DREAMS sensors were designed to operate in a very dusty environment, because the experiment was designed to operate on Mars during the dust storm season (October 2016 in Meridiani Planum). Unfortunately, the Schiaparelli module failed part of the descent and the landing and crashed onto the surface of Mars. Nevertheless, several seconds before the crash, the module central computer switched the DREAMS instrument on, and sent back housekeeping data indicating that the DREAMS sensors were performing nominally. This article describes the instrument in terms of scientific goals, design, working principle and performances, as well as the results of calibration and field tests. The spare model is mature and available to fly in a future mission.

Abstract:

The InSight mission is due to launch in May 2018, carrying a payload of novel instruments designed and tested to probe the interior of Mars whilst deployed directly on the Martian regolith and partially isolated from the Martian environment by the Wind and Thermal Shield. Central to this payload is the seismometry package SEIS consisting of two seismometers, which is supported by a suite of environmental/meteorological sensors (Temperature and Wind Sensor for InSight TWINS; and Auxiliary Payload Sensor Suite APSS). In this work, an optimal estimations inversion scheme which aims to decorrelate the short-period seismometer (SEIS-SP) signal due to seismic activity alone from the environmental signal and random noise is detailed, and tested on both simulated and Viking data. This scheme also applies a module to identify measurements contaminated by Single Event Phenomena (SEP). This scheme will be deployed as the pre-processing pipeline for all SEIS-SP data prior to release to the scientific community for analysis.