Ares I-X Best Estimated Trajectory Analysis and Results

The Ares I-X trajectory reconstruction produced best estimated trajectories of the flight test vehicle ascent through stage separation, and of the first and upper stage entries after separation. The trajectory reconstruction process combines on-board, ground-based, and atmospheric measurements to produce the trajectory estimates. The Ares I-X vehicle had a number of on-board and ground based sensors that were available, including inertial measurement units, radar, air-data, and weather balloons. However, due to problems with calibrations and/or data, not all of the sensor data were used. The trajectory estimate was generated using an Iterative Extended Kalman Filter algorithm, which is an industry standard processing algorithm for filtering and estimation applications. This paper describes the methodology and results of the trajectory reconstruction process, including flight data preprocessing and input uncertainties, trajectory estimation algorithms, output transformations, and comparisons with preflight predictions. INTRODUCTION The Ares I rocket was designed as part of NASA’s Constellation Program to launch the Orion capsule containing 4 to 6 crew members to Low Earth Orbit (LEO) to support missions to the International Space Station, the moon, and beyond. A series of uncrewed test flights were planned to gather system performance data that cannot otherwise be obtained in ground testing. The first of these test flights, Ares I-X, occurred on October 28th, 2009. A critical component to the performance evaluation of the Flight Test Vehicle (FTV) is the Best Estimated Trajectory (BET), which is reconstructed from all available flight data. The Ares I-X trajectory reconstruction produced best estimated trajectories of the flight test vehicle ascent through stage separation, and of the first and upper stage entries after separation. The trajectory reconstruction process combines on-board, ground-based, and atmospheric measurements to produce the trajectory estimates. These measurements arise from a number of on-board and ground based sensors, including inertial measurement units, radar, air-data, and weather balloons. The BET was generated using an Iterative Extended Kalman Filter (IEKF) algorithm which is an industry standard processing algorithm for filtering and estimation applications. The IEKF algorithm is discussed in greater detail in later sections, but at a very high level, the algorithm amounts to a recursive weighted least-squares fit to all input data. Inputs to the BET filter consisted of data from on-board inertial measurement units, an on-board air data sensor, and radar at multiple ground tracking stations. Accelerations and angular rates from several inertial measurement units were integrated to provide an estimate of the vehicle state as a function of time. Tracking data from several ground-based radar stations and data Approved for public release; distribution is unlimited.