Sarah E. Lane

Diagnostic Capabilities for Electromagnetic Railguns

In the past 30 years, there have been significant advances in the development of modeling and simulation algorithms for electromagnetic railguns. The development of instrumentation capable of measuring the physical parameters that occur during a high-velocity launch, however, has not kept pace with the advances in modeling capabilities. In addition, there has been an increase in the size and complexity of existing railguns, and therefore it has become necessary to find instrumentation that has the flexibility to conform to the variations present from one railgun to the next, to aid in the cross-utilization of instrumentation across the community. This paper will describe results from Georgia Tech and U.S. Navy to evaluate diagnostic techniques that measure different phenomena at higher resolution in both time and space in order to provide the data needed to validate railgun models. The diagnostics described here address all aspects of railgun testing, including the launcher, projectile, and pulsed power supplies and all phases of the evaluation process from validation of modeling and simulation tools to structural health monitoring. Specific quantities for which diagnostics will be described include temperature, electric and magnetic field sensors, and strain measurements. Examples of electromagnetic sensors that will be presented include colossal magnetoresistance sensors, which respond to changes in a magnetic field with a change in resistance, and a slab-coupled optical sensor for detecting electric fields. Test results from railguns at both Georgia Tech and the French-German Institute in Saint-Louis will be described.

Diagnostic capabilities for electromagnetic railguns

In the past 30 years, there have been significant advances in the development of modeling and simulation algorithms for electromagnetic railguns. The development of instrumentation capable of measuring the physical parameters that occur during a high velocity launch, however, has not kept pace with the advances in modeling capabilities. In addition, there has been an increase in the size and complexity of existing railguns and therefore it has become necessary to find instrumentation that has the flexibility to conform to the variations present from one railgun to the next, to aid in the cross-utilization of instrumentation across the community. This paper will describe results from Georgia Tech and Navy efforts to evaluate diagnostic techniques that measure different phenomenon at higher resolution in both time and space in order to provide the data needed to validate railgun models. The diagnostics described here address all aspects of railgun testing, including the launcher, projectile, and pulsed power supplies and all phases of the evaluation process from validation of modeling and simulation tools to structural health monitoring. Specific quantities for which diagnostics will be described include temperature, electric and magnetic field sensors, and strain measurements. Examples of electromagnetic sensors that will be presented include colossal magneto-resistance (CMR) sensors, which respond to changes in a magnetic field with a change in resistance, and a slab coupled optical sensor (SCOS) for detecting electric fields. Test results from railguns at both Georgia Tech and ISL will be described.

OREOS: a new EO-IR modeling and simulation tool for U.S. Coast Guard search and rescue applications

Georgia Tech has developed a new modeling and simulation tool that predicts both radar and electro-optical infrared (EO-IR) lateral range curves (LRCs) and sweep widths (SWs) under the Optimization of Radar and Electro-Optical Sensors (OREOS) program for US Coast Guard Search and Rescue (SAR) applications. In a search scenario when the location of the lost or overdue craft is unknown, the Coast Guard will conduct searches based upon standard procedure, personnel expertise, operational experience, and models. One metric for search planning is the sweep width, or integrated area under a LRC. Because a searching craft is equipped with radar and EO-IR sensor suites, the Coast Guard is interested in accurate predictions of sweep width for the particular search scenario. Here, we will discuss the physical models that make up the EO-IR portion of the OREOS code. First, Georgia Tech SIGnature (GTSIG) generates thermal signatures of search targets based upon the thermal and optical properties of the target and the environment; a renderer then calculates target contrast. Sensor information, atmospheric transmission, and the calculated target contrasts are input into NVESD models to generate probability of detection (PD) vs. slant range data. These PD vs. range values are then converted into LRCs by taking into account a continuous look search from a moving platform; sweep widths are then calculated. The OREOS tool differs from previous methods in that physical models are used to predict the LRCs and sweep widths at every step in the process, whereas heuristic methods were previously employed to generate final predictions.

Hyperspectral image turbulence measurements of the atmosphere

A Forward Looking Interferometer (FLI) sensor has the potential to be used as a means of detecting aviation hazards in flight. One of these hazards is mountain wave turbulence. The results from a data acquisition activity at the University of Colorados Mountain Research Station will be presented here. Hyperspectral datacubes from a Telops Hyper-Cam are being studied to determine if evidence of a turbulent event can be identified in the data. These data are then being compared with D&P TurboFT data, which are collected at a much higher time resolution and broader spectrum.

Detection of aircraft exhaust in hyperspectral image data

The use of a hyperspectral imaging system for the detection of gases has been investigated, and algorithms have been developed for various applications. Of particular interest here is the ability to use these algorithms in the detection of the wake disturbances trailing an aircraft. A dataset of long wave infrared (LWIR) hyperspectral datacubes taken with a Telops Hyper-Cam at Hartsfield-Jackson International Airport in Atlanta, Georgia is investigated. The methodology presented here assumes that the aircraft engine exhaust gases will become entrained in wake vortices that develop; therefore, if the exhaust can be detected upon exiting the engines, it can be followed through subsequent datacubes until the vortex disturbance is detected. Gases known to exist in aircraft exhaust are modeled, and the Adaptive Coherence/Cosine Estimator (ACE) is used to search for these gases. Although wake vortices have not been found in the data, an unknown disturbance following the passage of the aircraft has been discovered.

Landing zone identification for autonomous UAV applications using fused hyperspectral imagery and LIDAR point clouds

Multi-modal data fusion for situational awareness is of interest because fusion of data can provide more information than the individual modalities alone. However, many questions remain, including what data is beneficial, what algorithms work the best or are fastest, and where in the processing pipeline should data be fused? In this paper, we explore some of these questions through a processing pipeline designed for multi-modal data fusion in an autonomous UAV landing scenario. In this paper, we assess landing zone identification methods using two data modalities: hyperspectral imagery and LIDAR point clouds. Using hyperspectral image and LIDAR data from two datasets of Maui and a university campus, we assess the accuracies of different landing zone identification methods, compare rule-based and machine learning based classifications, and show that depending on the dataset, fusion does not always increase performance. However, we show that machine learning methods can be used to ascertain the usefulness of individual modalities and their resulting attributes when used to perform classification.

Spectral emissivity changes from weathering

Spectral classification of thermal band remote sensing data requires knowledge of the spectral emissivity properties of the representative materials. Open source libraries of spectral emissivity properties of some materials have been produced but frequently these measurements have been made on unweathered samples. This study was designed to collect spectral emissivity data on a set of common materials for two purposes. One purpose was to collect data at different points in time during the weathering cycle; the second purpose was to monitor the temporal change rate in the emissivity properties. The intent of this latter effort was to determine the rate of change and the time at which a steady state was reached. Spectral emissivity data was collected across the thermal bands for a period of 18 months. Concurrently weather information was also collected. Reflectivity data changes were also measured and correlated against the thermal data. All the data was linked to cumulative weathering data.

Imaging FTIR emissivity measurement method

Though many materials behave approximately as greybodies across the long-wave infrared (LWIR) waveband, certain important infrared (IR) scene modeling materials such as brick and galvanized steel exhibit more complex optical properties1. Accurately describing how non-greybody materials interact relies critically on the accurate incorporation of the emissive and reflective properties of the in-scene materials. Typically, measured values are obtained and used. When measured using a non-imaging spectrometer, a given material’s spectral emissivity requires more than one collection episode, as both the sample under test and a standard must be measured separately. In the interval between episodes changes in environment degrade emissivity measurement accuracy. While repeating and averaging measurements of the standard and sample helps mitigate such effects, a simultaneous measurement of both can ensure identical environmental conditions during the measurement process, thus reducing inaccuracies and delivering a temporally accurate determination of background or ‘down-welling’ radiation. We report on a method for minimizing temporal inaccuracies in sample emissivity measurements. Using a LWIR hyperspectral imager, a Telops Hyper-Cam2, an approach permitting hundreds of simultaneous, calibrated spectral radiance measurements of the sample under test as well as a diffuse gold standard is described. In addition, we describe the data reduction technique to exploit these measurements. Following development of the reported method, spectral reflectance data from 10 samples of various materials of interest were collected. These data are presented along with comments on how such data will enhance the fidelity of computer models of IR scenes.