Shuttle subsonic horizontal wind estimation

Kelly, G. M.; Findlay, J. T.; Compton, H. R.

doi:10.2514/3.25612

Cited by 13 publications

(9 citation statements)

References 1 publication

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“…9 This singular condition is problematic for the MEADS application since the MSL entry trajectory crosses zero flight path angle on two occasions, shown in Fig. 1(c).…”

Section: Wind Estimationmentioning

confidence: 97%

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Mars Entry Atmospheric Data System Modeling and Algorithm Development

Karlgaard

Beck

O’Keefe

et al. 2009

41st AIAA Thermophysics Conference

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The Mars Entry Atmospheric Data System (MEADS) is being developed as part of the Mars Science Laboratory (MSL), Entry, Descent, and Landing Instrumentation (MEDLI) project. The MEADS project involves installing an array of seven pressure transducers linked to ports on the MSL forebody to record the surface pressure distribution during atmospheric entry. These measured surface pressures are used to generate estimates of atmospheric quantities based on modeled surface pressure distributions. In particular, the quantities to be estimated from the MEADS pressure measurements include the total pressure, dynamic pressure, Mach number, angle of attack, and angle of sideslip. Secondary objectives are to estimate atmospheric winds by coupling the pressure measurements with the on-board Inertial Measurement Unit (IMU) data. This paper provides details of the algorithm development, MEADS system performance based on calibration, and uncertainty analysis for the aerodynamic and atmospheric quantities of interest. The work presented here is part of the MEDLI performance pre-flight validation and will culminate with processing flight data after Mars entry in 2012.

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“…9 This singular condition is problematic for the MEADS application since the MSL entry trajectory crosses zero flight path angle on two occasions, shown in Fig. 1(c).…”

Section: Wind Estimationmentioning

confidence: 97%

“…One approach is to relate the angle of attack and sideslip estimates to the planet relative velocity and the horizontal wind components and solve directly for the winds using Newton's method. 9 To this end, the aerodynamic angles can be expressed as…”

Section: Wind Estimationmentioning

confidence: 99%

Mars Entry Atmospheric Data System Modeling and Algorithm Development

Karlgaard

Beck

O’Keefe

et al. 2009

41st AIAA Thermophysics Conference

View full text Add to dashboard Cite

show abstract

“…However, wind speeds are also often important parameters of interest, and there are techniques that leverage FADS measurements and onboard IMU data to estimate these quantities [24]. Thus, the observability of wind speeds can also be a quantity that is added to the objective function.…”

Section: Optimizing For Low Dynamic Pressure and Wind Speed Recomentioning

confidence: 99%

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

Dutta

Braun

2014

Journal of Spacecraft and Rockets

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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 that serve as the point of diminishing returns. The methodology is tested with a simulated Mars entry, descent, and landing trajectory. Nomenclature f = objective function H = measurement sensitivity matrix k = number of sampled points for the objective function n = number of ports P = state covariance matrix p = parameter vector for the objective function R = measurement noise covariance matrix q ∞ = freestream dynamic pressure, Pa α = angle of attack, rad β = sideslip angle, rad σ = standard deviation Subscripts i, j = indices y = y component z = z component Superscriptŝ = nonnormalized value − = mean value

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“…However, wind speeds are also often important parameters of interest, and there are techniques that leverage FADS measurements and on-board IMU data to estimate these quantities. 24 Thus, the observability of wind speeds can also be a quantity that is added to the objective function. However, past studies have shown that the wind speed estimation is more a function of the IMU-based velocity reconstruction, 2 so other changes to the objective function have to be also made to reflect this situation.…”

Section: Optimizing For Low Dynamic Pressure and Wind Speed Recomentioning

confidence: 99%