Thesis (M.S.)--University of Wisconsin, 1965. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves [55-59]).

Many modern entry guidance systems depend on predictions of atmospheric parameters, notably atmospheric density, in order to guide the entry vehicle to some desired final state. However, in highly dynamic atmospheric environments such as the Martian atmosphere, the density may vary by as much as 200% from predicted pre-entry trends. This high level of atmospheric density uncertainty can cause significant complications for entry guidance processes and may in extreme scenarios cause complete failure of the entry. In the face of this uncertainty, mission designers are compelled to apply large trajectory and design safety margins which typically drive the system design towards less efficient solutions with smaller delivered payloads. The margins necessary to combat the high levels of atmospheric uncertainty may even preclude scientifically interesting destinations or architecturally useful mission modes such as aerocapture. Aerocapture is a method for inserting a spacecraft into an orbit about a planetary body with an atmosphere without the need for significant propulsive maneuvers. This can reduce the required propellant and propulsion hardware for a given mission which lowers mission costs and increases the available payload fraction. However, large density dispersions have a particularly acute effect on aerocapture trajectories due to the interaction of the high required speeds and relatively low densities encountered at aerocapture altitudes. Therefore, while the potential system level benefits of aerocapture are great, so too are the risks associated with this mission mode in highly uncertain atmospheric environments such as Mars. Contemporary entry guidance systems utilize static atmospheric density models for trajectory prediction and control. These static models are unable to alter the fundamental nature of the underlying state equations which are used to predict atmospheric density. This limits both the fidelity and adaptive freedom of these models and forces the guidance system to retroactively correct for the density prediction errors after those errors have already impacted the trajectory. A new class of dynamic density estimator called a Plastic Ensemble Neural System (PENS) is introduced which is able to generate high fidelity, adaptable density forecast models by altering the underlying atmospheric state equations to better agree with observed atmospheric trends. A new construct called an ensemble echo is also introduced which creates an associative learning architecture, permitting PENS to evolve with increasing atmospheric exposure. The PENS estimator is applied to a numerical guidance system and the performance of the composite system is investigated with over 144,000 guided trajectory simulations. The results demonstrate that the PENS algorithm achieves significant reductions in both the required post-aerocapture performance, and the aerocapture failure rates relative to historical density estimators.

A probability density function is developed for the phase of light that is the result of adding a signal to noise with K-distributed amplitude and uniform phase. The probability density function of the phase associated with the I-K distribution is also developed. In the process of deriving the probability density function of the phase much I as learned about the relationships between different probability density functions. Three different methods of deriving homodyned K statistics are shown to be equivalent. Two different methods of deriving I-K statistics are shown to be equivalent. Theoretical moments of the homodyned K distribution are compared with experimentally measured moments in order to determine the parameters of the model for different conditions of turbulence. An experiment is proposed for measuring the spatial structure function of the phase in a manner that will allow verifying the accuracy of the new probability density functions of the phase.

This document describes the design and deployment of a first generation water vapor density sensing unit, the HumiSense. This device is based on an open, air-filled capacitor which is part of a resonant circuit. The frequency of the resonant circuit mixed with a fixed frequency oscillator is the basis of the method to generate a signal that is associated to the change in water vapor density within the open capacitor with time. The physical testing results were inconclusive given that there were many unresolved artifacts in the data. Several suggestions for improving the device for future device generations were provided. / Graduation date: 2013