Planetary Probe Entry Atmosphere Estimation Using Synthetic Air Data System

Area of Science:

• Aerospace Engineering
• Planetary Science
• Atmospheric Science

Background:

• Accurate atmospheric state estimation is crucial for planetary entry, descent, and landing (EDL).
• Traditional methods often rely on pressure transducers, which can be vulnerable or add complexity.
• Existing aerodynamic models require atmospheric state information for accurate force and moment calculations.

Purpose of the Study:

• To develop and validate a new atmospheric state estimator using inertial measurements and an aerodynamic model.
• To demonstrate the feasibility of estimating atmospheric conditions without relying on pressure sensors.
• To assess the performance of the proposed method using real mission data.

Main Methods:

• Developed an atmospheric state estimator integrating inertial acceleration and angular rate data with a vehicle aerodynamic model.
• Recast the aerodynamic model to be solely dependent on the vehicle's navigation state and atmospheric conditions.
• Employed a Kalman-Schmidt filter to combine sensed forces and moments with the aerodynamic model for state estimation.

Main Results:

• The proposed estimator successfully estimated atmospheric conditions using inertial data and an aerodynamic model.
• Results were consistent with Flush Air Data Sensing (FADS) algorithms that use pressure measurements.
• The method demonstrated the capability to provide accurate atmospheric state estimates without pressure transducers.

Conclusions:

• The developed atmospheric state estimator offers a viable alternative to pressure-based methods for planetary missions.
• This approach has significant implications for future missions, enhancing EDL system robustness and potentially reducing mass and complexity.
• The method's validation on the Mars Science Laboratory mission provides confidence in its applicability to future Mars exploration endeavors.