The decision on whether to use a mid wave infrared (MWIR) or long wave infrared (LWIR) sensor for a given task can be a formidable verdict. The scope entails facts about the observable source, the atmospheric interactions, and the sensor parameters within the hardware device. Even when all the individual metrics are known, the combination ultimately determines whether a MWIR or LWIR sensor is more appropriate. Despite the vast number of variables at play, the reduction of inputs through focused studies can provide essential insight into MWIR and LWIR comparisons. This dissertation focuses on the roles of point source target detection, atmospheric scattering and absorption effects, and target identification has for MWIR vs LWIR performance. The point source analysis details the Pulse Visibility Factor (PVF) and how it affects the Signal to Noise (SNR) for Infrared Search and Track (IRST) tasks. The PVF is an essential parameter that not only depends upon camera system hardware but also the dynamics of the imaged point source target. The numerical predictions of the PVF show how the hardware transfer function spreads the point source object across the detector array. As a result, it is a critical aspect for MWIR vs LWIR IRST system performance. Atmospheric effects are another essential study for MWIR and LWIR imaging performance. Given the magnitude of atmospheric variables, the focus here is to reduce the atmospheric conditions with known particulates and concentrations to provide predictable results. The analysis details how a sparse aerosol medium can absorb and scatter incident light to produce a blur and compromise image quality. Predictions of the aerosol Modulation Transfer Function (MTF) detail the differences in MWIR vs LWIR performance due to aerosols. The MTFs are then added into the Night Vision Integrated Performance Model (NVIPM) to calculate the ability to identify a target at range for typical MWIR and LWIR sensors.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1187 |
| Date | 01 January 2020 |
| Creators | Butrimas, Steven |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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