The reconstruction of entry, descent and landing (EDL) trajectories is significantly affected by the knowledge of the atmospheric conditions during flight. Away from Earth, this knowledge is generally characterized by a high degree of uncertainty, which drives the accuracy of many important atmosphere-relative states. One method of obtaining the in-flight atmospheric properties during EDL is to utilize the known vehicle aerodynamics in deriving the trajectory parameters. This is the approach taken by this research in developing a methodology for accurate estimation of ambient atmospheric conditions and atmosphere-relative states. The method, referred to as the aerodynamic database (ADB) reconstruction, performs reconstruction by leveraging data from flight measurements and pre-flight models. In addition to the estimation algorithm, an uncertainty assessment for the ADB reconstruction method is developed. This uncertainty assessment is a unique application of a fundamental analysis technique that applies linear covariance mapping to transform input variances into output uncertainties. The ADB reconstruction is applied to a previous mission in order to demonstrate its capability and accuracy. Flight data from the Mars Science Laboratory (MSL) EDL, having successfully completed on August 5th 2012, is used for this purpose. Comparisons of the estimated states are made against alternate reconstruction approaches to understand the advantages and limitations of the ADB reconstruction. This thesis presents a method of reconstruction for EDL systems that can be used as a valuable tool for planetary entry analysis.

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There is significant uncertainty in our knowledge of the Martian atmosphere and the aerodynamics of the Mars entry, descent, and landing (EDL) systems. These uncertainties result in conservatism in the design of the EDL vehicles leading to higher system masses and a broad range of performance predictions. Data from flight instrumentation onboard Mars EDL systems can be used to quantify these uncertainties, but the existing dataset is sparse and many parameters of interest have not been previously observable. Many past EDL reconstructions neither utilize statistical information about the uncertainty of the measured data nor quantify the uncertainty of the estimated parameters. Statistical estimation methods can blend together disparate data types to improve the reconstruction of parameters of interest for the vehicle. For example, integrating data obtained from aeroshell-mounted pressure transducers, inertial measurement unit, and radar altimeter can improve the estimates of the trajectory, atmospheric profile, and aerodynamic coefficients, while also quantifying the uncertainty in these estimates. These same statistical methods can be leveraged to improve current engineering models in order to reduce conservatism in future EDL vehicle design. The work in this thesis presents a comprehensive methodology for parameter reconstruction and uncertainty quantification while blending dissimilar Mars EDL datasets. Statistical estimation methods applied include the Extended Kalman Filter, Unscented Kalman Filter, and Adaptive Filter. The estimators are applied in a manner in which the observability of the parameters of interest is maximized while using the sparse, disparate EDL dataset. The methodology is validated with simulated data and then applied to estimate the EDL performance of the 2012 Mars Science Laboratory. The reconstruction methodology is also utilized as a tool for improving vehicle design and reducing design conservatism. A novel method of optimizing the design of future EDL atmospheric data systems is presented by leveraging the reconstruction methodology. The methodology identifies important design trends and the point of diminishing returns of atmospheric data sensors that are critical in improving the reconstruction performance for future EDL vehicles. The impact of the estimation methodology on aerodynamic and atmospheric engineering models is also studied and suggestions are made for future EDL instrumentation.

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