Airborne Wireless Communication Modeling and Analysis with MATLAB

Over the past decade, there has been a dramatic increase in the use of unmanned aerial vehicles (UAV) for military, commercial, and private applications. Critical to maintaining control and a use for these systems is the development of wireless networking systems [1]. Computer simulation has increasingly become a key player in airborne networking developments though the accuracy and credibility of network simulations has become a topic of increasing scrutiny [2-5]. Much of the inaccuracies seen in simulation are due to inaccurate modeling of the physical layer of the communication system. This research develops a physical layer model that combines antenna modeling using computational electromagnetics and the two-ray propagation model to predict the received signal strength. The antenna is modeled with triangular patches and analyzed by extending the antenna modeling algorithm by Sergey Makarov, which employs Rao-Wilton-Glisson basis functions. The two-ray model consists of a line-of-sight ray and a reflected ray that is modeled as a lossless ground reflection. Comparison with a UAV data collection shows that the developed physical layer model improves over a simpler model that was only dependent on distance. The resulting two-ray model provides a more accurate networking model framework for future wireless network simulations

The Challenges of Low-Energy Secondary Electron Emission Measurement

The phenomena known as secondary electron emission was discovered over a century ago. Yet it remains very difficult to model accurately due to the limited availability of reliable experimental data. With the growing use of computer simulations in hardware development, the need for accurate models has increased. This research focused on determining what factors may be causing measurement discrepancies and methods for increasing the accuracy of measurements. It was found that several assumptions are commonly invoked when these measurements are performed that may not always be consistent with reality. The violation of these assumptions leads to measurement bias that is contingent upon the apparatus and the voltages used during the measurement. This research showed that secondary electron yield measurements are sensitive to changes in the apparatus geometry, the current level, and the electron gun settings. New techniques, hardware, and models were developed in order facilitate greater measurement repeatability and accuracy