Ricky A. Goodson

Examining the sensitivity of simulated surface temperatures due to meteorological conditions

The U.S. Army Engineer Research and Development Center (ERDC) has developed a suite of models that replicate the signicant geo-physical processes which aect the thermal signatures sensed by infrared imaging systems. This suite of models also includes an electro-optical/infrared (EO/IR) sensor model that produces synthetic thermal imagery. The EO/IR sensor model can be adapted to replicate the performance of other infrared sensor systems as well. It is well known that eld-collected IR imagery can be in uenced by the micro-topographic features of a particular location. As a result, the performance of automated target recognition algorithms and decisions based on their results can also be aected. Other signicant contributors to false alarms and issues with probabilities-of- detection include the relative locations of vegetation and local changes in soil types or properties. For example, a change in the retention of soil moisture alone is known to contribute to false alarms due to changes in radiative and thermal properties of wet versus dry soil. Many aspects of eld data collection eorts (weather, soil uniformity, etc.) cannot be controlled nor changed after the fact. Within a computational framework, however, plant and object locations, as well as weather patterns can, all be changed. In this work, the sensitivity of simulated IR imagery will be examined as it relates to initial states and boundary forcing terms due to weather conditions. Dierent approaches to these inputs will be examined using the computational testbed developed at the ERDC.

Overview of computational testbed for evaluating electro-optical/infrared sensor systems

The U.S. Army Engineer Research and Development Center (ERDC) developed a near-surface computational testbed (CTB) for modeling geo-environments. This modeling capability is used to predict and improve the performance of current and future-force sensor systems for surface and near-surface threat detection for a wide range of geoenvironments. The CTB is a suite of integrated models and tools used to approximately replicate geo-physical processes such as radiometry, meteorology, moisture transport, and thermal transport that influence the resultant signatures of both natural and man-made materials, as perceived by the sensors. The CTB is designed within a High Performance Computing (HPC) framework to accommodate the size and complexity of the virtual environments required for analyzing and quantifying sensor performance. Specifically, as a rule-of-thumb, the size of the scene should encompass an area that is at a minimum, the size of the spatial coverage of the sensor. This HPC capability allows the CTB to replicate geophysical processes and subsurface heterogeneity with high levels of realism and to provide new insight into identifying the geophysical processes and environmental factors that significantly affect the signatures sensed by multispectral imaging, near-infrared, mid-wave infrared, long-wave infrared, and ground penetrating radar sensors. Additionally, this effort is helping to quantify the performance and optimal time-of-use for sensors to detect threats within highly heterogeneous geo-environments by reducing false alarms from automated target recognition algorithms.

Signature Evaluation for Thermal Infrared Countermine and IED Detection Systems: Large-Area Simulations in an Operational Computing Environment

The countermine test bed (CTB) and accompanying tools provide a means to optimize thermal infrared sensor systems and automated target recognition algorithms. The CTB has been validated through a series of studies conducted since 2006. During that time, the capability of the CTB has been vastly expanded, particularly in regards to the size of the domain that can be modeled. The CTB consists of four independent models that are coupled through file transfers. Optimization of the system involves a scheduling problem whereby the processors are assigned to individual sub-models in accordance with their run times. The ground model and the ray caster dominate computations, with the other sub-models operating virtually as background processes.

Analysis of GEM-3 data collected for the advanced UXO detection/discrimination technology demonstration: U.S. Army Jefferson Proving Ground, Madison, Indiana

This paper analyzes the UXO classification capabilities of the GEM-3 using data collected for the Advanced UXO Detection/Discrimination Technology Demonstration at the U.S. Army Jefferson Proving Ground (JPG), Madison, Indiana. The approach taken in the US Army Engineer Research and Development Center (ERDC) analysis of the performance of the GEM-3 at JPG was to extract data points collected near each of the actual target locations and compare them to the calibration data acquired with known targets at the beginning of the demonstration. This was done to determine how well the data collected near each actual target matched the calibration signatures for the same ordnance type and the extent to which the data could be differentiated from other ordnance types and non-ordnance clutter. Classification of the targets was performed using a simple template-matching algorithm. This procedure resulted in an exact classification match for nearly half of the targets for which calibration data were available and a match to a similarly sized target for more than two-thirds of the medium and large targets. The sensor coverage of the test areas and the effect of test parameters such as ordnance size and depth on classification performance were also examined. New data were acquired with the GEM-3 to investigate the statistical variability of the instrument.