An analysis of low-frequency component in microacceleration measurements made onboard the <Emphasis Type="Italic">Foton M-2</Emphasis> satellite

The low-frequency component is investigated in the data of measurements performed onboard the Foton M-2 satellite with the three-component accelerometer TAS-3. Investigations consisted in comparison of this component with its calculated analog found from a reconstruction of the satellite’s attitude motion. The influence of the Earth’s magnetic field on the accelerometer readings is discovered by way of spectral analysis of the functions representing the results of determining the low-frequency microacceleration by two methods. After making correction for this influence, the results obtained by these two methods coincided within a root-mean-square error of less than 10^?6 m/s².     

Estimation of dynamic characteristics of the <Emphasis Type="Italic">International Space Station</Emphasis> from measurements of microaccelerations

We describe the results of determining the mass of the International Space Station using the data of MAMS accelerometer taken during correction of the station orbit on August 20, 2004. The correction was made by approach and attitude control engines (ACE) of the Progress transporting spacecraft. The engines were preliminary calibrated in an autonomous flight using the onboard device for measuring apparent velocity increment. The method of calibration is described and its results are presented. The error in station mass determination is about 1%. The same data of MAMS and similar data obtained during the orbit correction on August 26, 2004 were used for the analysis of high-frequency vibrations of the station mainframe caused by operation of the ACE of Progress. Natural frequencies of the ACE are determined. They lie in the frequency band 0.024–0.11 Hz. ACE operation is demonstrated to result in a substantial increase of microaccelerations onboard the station in the frequency range 0–1 Hz. The frequencies are indicated at which disturbances increase by more than an order of magnitude. The study described was carried out as a part of the Tensor technological experiment.     

Aircraft engine health management via stochastic modelling of flight data interrelations

A novel engine health management (EHM) scheme is introduced. It uses flight-level, instead of thermodynamic, data to cost-effectively augment the onboard EHM redundancy. For a nominal healthy aircraft, fault-sensitive interrelations among flight data are globally modelled inside a flight regime via Constant-Coefficient Pooled Nonlinear AutoRegressive with eXogenous (CCP-NARX) excitation representations. Single or sequential engine faults perturb these interrelations. Statistically evaluating the perturbation-induced effects draws reliable conclusions on the engine?s health. Validation and comparisons with Kalman filter-based alternatives are made throughout the regime under various operational conditions.     

ExoMars 2016 Schiaparelli Module Trajectory and Atmospheric Profiles Reconstruction

On 19^th October 2016 Schiaparelli module of the ExoMars 2016 mission flew through the Mars atmosphere. After successful entry and descent under parachute, the module failed the last part of the descent and crashed on the Mars surface. Nevertheless the data transmitted in real-time by Schiaparelli during the entry and descent, together with the entry state vector as initial condition, have been used to reconstruct both the trajectory and the profiles of atmospheric density, pressure and temperature along the traversed path.The available data-set is only a small sub-set of the whole data acquired by Schiaparelli, with a limited data rate (8 kbps) and a large gap during the entry because of the plasma blackout on the communications.This paper presents the work done by the AMELIA (Atmospheric Mars Entry and Landing Investigations and Analysis) team in the exploitation of the available inertial and radar data. First a reference trajectory is derived by direct integration of the inertial measurements and a strategy to overcome the entry data gap is proposed. First-order covariance analysis is used to estimate the uncertainties on all the derived parameters. Then a refined trajectory is computed incorporating the measurements provided by the on-board radar altimeter.The derived trajectory is consistent with the events reported in the telemetry and also with the impact point identified on the high-resolution images of the landing site.Finally, atmospheric profiles are computed tacking into account the aerodynamic properties of the module. Derived profiles result in good agreement with both atmospheric models and available remote sensing observations.