Soumyo Dutta

Mars Entry, Descent, and Landing Trajectory and Atmosphere Reconstruction

Flight data from an entry, descent, and landing (ED L) sequence can be used to reconstruct the vehicle’s trajectory as well as com pute the associated uncertainty. The atmospheric profile encountered by the vehicle can also be estimated from flight data. Past Mars missions have contained instruments, such as accelerometers, gyroscopes, and radar altimeters that do not provide direct measurement o f the free-stream atmospheric conditions. Thus, uncertainties in the atmospheric reconstruction and the aerodynamic database knowledge cannot be separated. However, the upcoming Mars Science Laboratory (MSL) will take measurements of the pressure on the aeroshell forebody during entry. These measurements will provide means to determine the free-stream conditions and to separate the atmospheric and aerodynamic uncertainties. In this paper, analytical methods to statistically determine trajectories and free-strea m conditions from flight data and to quantify uncertainties in these estimates are discu ssed. A sample data set from the ballistic range test of Orion Crew Exploration Vehicle (CEV) is then used to demonstrate results from applying these procedures. This approach utilizes the same techniques and toolset planned for subsequent application for the reconstr uction of MSL’s EDL sequence in 2012.

Comparison of Statistical Estimation Techniques for Mars Entry, Descent, and Landing Reconstruction

Flight data from an entry, descent, and landing sequence can be used to reconstruct the vehicles trajectory, aerodynamic coefficients, and the atmospheric profile experienced by the vehicle. Past Mars missions have not contained instrumentation that would allow for the separation of uncertainties in the atmosphere and the aerodynamic database. The 2012 Mars Science Laboratory took measurements of the pressure distribution on the aeroshell forebody during entry and allows freestream atmospheric conditions to be partially observable. Methods to estimate the flight performance statistically using onboard measurements are demonstrated here through the use of simulated Mars data. A range of statistical estimators, specifically the extended Kalman filter and unscented Kalman filter, are used to demonstrate which estimator best quantifies the states and the uncertainties in the flight parameters. The techniques demonstrated herein are planned for application to the Mars Science Laboratory flight dataset.

Uncertainty Quantication for Mars Entry, Descent, and Landing Reconstruction Using Adaptive Filtering

Mars entry, descent, and landing trajectories are highly dependent on the vehicle’s aerodynamics and the planet’s atmospheric properties during the day of flight. A majority of previous Mars entry trajectory and atmosphere reconstruction analyses do not simultaneously estimate the flight trajectory and the uncertainties in the atmospheric and aerodynamics models. Adaptive filtering techniques, when combined with traditional trajectory estimation methods, can improve the knowledge of the aerodynamic coefficients and atmospheric properties, while also estimating the confidence interval for these parameters. Simulated data sets with known truth data are used in this study to show the improvement in state and uncertainty estimation by using adaptive filtering techniques. Such a methodology can then be implemented on existing and future Mars entry data sets to determine the aerodynamic and atmospheric uncertainties and improve engineering design tools.

Comparison of Statistical Estimation Techniques for Mars Entry, Descent, and Landing Reconstruction from MEDLI-like Data Sources

Flight data from an entry, descent, and landing (EDL) sequence can be used to reconstruct the vehicles trajectory, aerodynamic coefficients and the atmospheric profile experienced by the vehicle. Past Mars missions have contained instruments that do not provide direct measurement of the freestream atmospheric conditions. Thus, the uncertainties in the atmospheric reconstruction and the aerodynamic database knowledge could not be separated. The upcoming Mars Science Laboratory (MSL) will take measurements of the pressure distribution on the aeroshell forebody during entry and will allow freestream atmospheric conditions to be partially observable. This data provides a mean to separate atmospheric and aerodynamic uncertainties and is part of the MSL EDL Instrumentation (MEDLI) project. Methods to estimate the flight performance statistically using on-board measurements are demonstrated here through the use of simulated Mars data. Different statistical estimators are used to demonstrate which estimator best quantifies the uncertainties in the flight parameters. The techniques demonstrated herein are planned for application to the MSL flight dataset after the spacecraft lands on Mars in August 2012.

Analytically-derived Aerodynamic Force and Moment Coecients of Resident Space Objects in Free-Molecular Flow

acting on a general body in free-molecular regime to derive aerodynamic force and moment expressions. The analytical aerodynamics prediction method is described and relations have been developed for the sphere, cylinder, panel, and rectangular prism. The NASA-developed Direct Simulation Monte Carlo Analysis Code is used to validate the analytical expressions and it is shown that expressions are accurate within 0.38%. These generalized analytic expressions in terms of angle of attack, sideslip angle, freestream conditions, wall temperature, and accommodation coecients allow near-instantaneous computation of the rareed aerodynamics and enables space situation awareness analysis.

Cramér–Rao Lower-Bound Optimization of Flush Atmospheric Data System Sensor Placement

Flush atmospheric data systems take measurements of the pressure distribution on the forebodies of vehicles and improve the estimate of freestream parameters during reconstruction. These systems have been present on many past entry vehicles, but design of the pressure transducer suites and the placement of the sensors on the vehicle forebody have largely relied on engineering judgment and heuristic techniques. This paper develops a flush atmospheric data system design methodology using Cramer–Rao lower-bound optimization to define the smallest theoretical variance possible from the estimation process. Application of this methodology yields Pareto frontiers of possible optimal configurations and identifies the number of ports that serve as the point of diminishing returns. The methodology is tested with a simulated Mars entry, descent, and landing trajectory.

LDSD supersonic Flight Dynamics Test 1: Post-flight reconstruction

The Low Density Supersonic Decelerator projects first Supersonic Flight Dynamics Test (SFDT) occurred on June 28, 2014, off the west coast of Kauai, Hawaii, over the Pacific Ocean. The test vehicle traveled to speeds above Mach 4 and to an altitude of over 200,000 feet. This flight, although classified as a test architecture shake-out flight, tested two technologies: a robotic class Supersonic Inflatable Aerodynamic Decelerator and a Supersonic Disksail Parachute. The reconstruction team was tasked with collecting all relevant pre-flight and flight data to accurately reconstruct the trajectory and technology performance during the science phase of the flight. Furthermore, the reconstruction team has been involved with reconstructing and exploring all aerodynamic and test vehicle properties that affected the entire flight phase. This reconstruction provided insight into the technology performance, which is a key deliverable for the LDSD project, as well as provided insight into lessons learned for subsequent SFDT flights, in the fields of data recovery, reconstruction, and pre-flight trajectory simulations.

Atmospheric Data System Sensor Placement Optimization for Mars Entry, Descent, and Landing

The Mars Science Laboratory (MSL) contains an atmospheric data system that takes measurement of the pressure distribution on the entry body during the hypersonic and supersonic descent phases of the ight. This pressure data can be combined with other onboard sensors, such as accelerometers, gyros, and radar altimeter, to estimate the ight’s trajectory, aerodynamics and the atmospheric pro le. The number of sensors and their locations for the atmospheric data system can be optimized to increase the accuracy of the postight reconstruction. Methodologies based on using the estimation residual and a surrogate of the observability matrix are presented here and results of the optimization exercises for pressure transducer systems on Mars entry, descent, and landing (EDL) vehicles are shown. These techniques can be subsequently applied in the design of instrumentation suites of future EDL vehicles.

Statistical Entry, Descent, and Landing Performance Reconstruction of the Mars Science Laboratory

The Mars Science Laboratory spacecraft landed an approximately 900 kg rover on Mars on August 5, 2012 while using the largest aeroshell and supersonic parachute ever utilized by a planetary entry mission. Similar to past Mars missions, the spacecraft recorded inertial measurement unit data and radar altimeter measurements during its descent through the Martian atmosphere, but its aeroshell was also instrumented with flush atmospheric data system sensors that captured the pressure distribution on the vehicle during hypersonic and supersonic flight regimes. The rich data set enabled a comprehensive post flight analysis of the vehicle’s trajectory. This paper shows the vehicle’s reconstructed trajectory, aerodynamics, and atmospheric conditions using several statistical estimation methods, specifically the Extended Kalman filter, Unscented Kalman filter, and adaptive filter. The statistical estimation methods allow for both state estimation and uncertainty quantification of model errors, which could improve design of future Mars entry missions.

Cramer-Rao Lower Bound Optimization of Flush Atmospheric Data System Sensor Placement

Flush atmospheric data systems take measurements of the pressure distribution on the forebodies of vehicles and improve the estimate of freestream parameters during reconstruction. These systems have been present on many past entry vehicles, but design of the pressure transducer suites and the placement of the sensors on the vehicle forebody have largely relied on engineering judgment and heuristic techniques. This paper develops a flush atmospheric data system design methodology using Cramér-Rao lower bound optimization to define the smallest theoretical variance possible from the estimation process. Application of this methodology yields Pareto frontiers of possible optimal configurations and identifies the number of ports which serve as the point of diminishing returns. The methodology is tested with a simulated Mars entry, descent, and landing trajectory.