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

Journal of Spacecraft and Rockets

Karlgaard

2014

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Mars entry, descent, and landing (EDL) trajectories are highly dependent on the vehicle's aerodynamics and the planet's atmospheric properties during the day-of-flight. A majority of previous EDL trajectory and atmosphere reconstruction analyses do not simultaneously estimate the flight trajectory and the uncertainties in the models. Adaptive filtering techniques, when combined with the traditional trajectory estimation methods, can improve the knowledge of the aerodynamic coefficients and atmospheric properties, while also estimating a realistic confidence interval for these parameters. Simulated datasets 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 EDL datasets to determine the aerodynamic and atmospheric uncertainties and improve engineering design tools.

Section: Motivationmentioning

confidence: 99%

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

Dutta

Journal of Spacecraft and Rockets

Karlgaard

2014

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“…Inverse methods have been widely used to solve data analysis problems in a broad range of fields such as heat transfer [14][15][16][17][18], geophysics [19], trajectory reconstruction [20,21], remote sensing, mathematics, and astronomy. The general methods used in these fields are similar.…”

Section: Inverse Estimation Methodsmentioning

confidence: 99%

Time-Dependent Mars Entry Aeroheating Estimation from Simulated In-Depth Heat Shield Temperature Measurements

Mahzari

Journal of Thermophysics and Heat Transfer

2013

There are substantial uncertainties in the computational models currently used to predict the heating environment and the Thermal Protection System material response during Mars entry. Flight data are required to quantify and possibly reduce such uncertainties as well as improve current computational tools. The Mars Science Laboratory Entry, Descent, and Landing Instrumentation suite will provide a comprehensive set of flight data that will include subsurface temperature measurements of its Phenolic Impregnated Carbon Ablator heatshield at different locations. The purpose of this paper is to investigate the time-dependent estimation of Mars Science Laboratory surface heating from simulated Mars Science Laboratory Entry, Descent, and Landing Instrumentation temperature data using inverse methods in the presence of random and bias measurement and model errors. The surface heat flux is indirectly reconstructed by estimating the discretized heat transfer coefficient profile as a function of time. Whole-time domain least-squares methods in conjunction with the Tikhonov regularization technique are applied to this problem. The performance of the estimation methods and the accuracy of the reconstructed surface conditions are investigated under different types of errors in the measurements, such as random noise and thermocouple lag. Furthermore, the effect of material property bias on the estimation of surface conditions is also studied.

“…The complete set of EOMs can be found in Ref. 4. Discussion about the development of the dynamic equations for freestream pressure and density as well as assumptions made can be found in Refs.…”

Section: B Process and Measurement Equationsmentioning

confidence: 99%

“…Gyro rates are used in the process equations (as discussed in Ref. 4) and thus no measurement equations are needed for it. However, the other three data types need some form of equation where the estimation state vector could be used to predict measurement values.…”

Section: B Process and Measurement Equationsmentioning

confidence: 99%

“…Together with the other on-board sensors, the pressure data can provide a good reconstruction of the flight's dynamic pressure, angle of attack, and sideslip angle without making any assumptions about the vehicle's aerodynamics and provides means of separating aerodynamic uncertainties from atmospheric uncertainties. 3,4 The MEADS instrumentation suite was designed for the MSL trajectory 2, 3 based on past Earth-based air data system designs and expert knowledge. However, there is a lack of work in the area of optimizing the locations of pressure transducers for an atmospheric data system.…”

mentioning

confidence: 99%

See 1 more Smart Citation

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

Dutta

AIAA Atmospheric Flight Mechanics Conference

Karlgaard

2012

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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 flight. This pressure data can be combined with other onboard sensors, such as accelerometers, gyros, and radar altimeter, to estimate the flight's trajectory, aerodynamics and the atmospheric profile. The number of sensors and their locations for the atmospheric data system can be optimized to increase the accuracy of the post-flight 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. Nomenclature AProcess eq. Jacobian (w.r.t. the state vector) BProcess eq. Jacobian (w.r.t. the noise vector)