US Army Corps of Engineers
Engineer Research and Development Center

Computational Test Bed

Published Nov. 20, 2012
To ensure accuracy, CTB collects a wide range of data for verification and scene generation, including meteorological data, vegetation maps, spectral data, soil properties, LIDAR data, Radar data, and electro-optical infrared imagery.

To ensure accuracy, CTB collects a wide range of data for verification and scene generation, including meteorological data, vegetation maps, spectral data, soil properties, LIDAR data, Radar data, and electro-optical infrared imagery.

As part of CTB, the MWIR Sensor Model produces synthetic images with a high level of realism.

As part of CTB, the MWIR Sensor Model produces synthetic images with a high level of realism.

Testing Models to Improve Force Sensor Systems

High fidelity models are needed to predict and improve performance of current and future force sensor systems for surface and near-surface target detection within complex geo-environmental settings.

Understand Phenomena Detected by Sensor Systems

Developed by the Geo-Environmental Tactical Sensor Simulation (GEOTACS) Army Technology Objective (ATO) program at the ERDC Geotechnical and Structures Laboratory (GSL) in Vicksburg, Miss., the Computational Test Bed (CTB) is a suite of 3-D, first principles, physics-based, high-fidelity models to predict and improve the performance of current and future-force sensor systems when detecting surface and near-surface objects in complex environments.

The CTB helps users understand the geophysical phenomena behind the signatures detected by sensors operating in the electromagnetic spectrum. The CTB models the physical processes at work in the environment and how these processes affect signatures sensed by passive and active sensor modalities.

Simulate Environments with Numerical Modeling Tools

The CTB consists of a scene generation tool, the Computational Model Builder (CMB) suite, and a collection of models to conduct physics-based simulations of various operational environments. The CTB’s geo-environmental modeling capability produces 3-D, physics-based, high-fidelity, numerical simulations of geo-environments by using parallelized codes operating on high performance computing resources. The synthetic scenes produced by these codes are sampled by sensor models, which produce synthetic imagery and reflected voltage as a function of time. This data is used to evaluate their performance and understand the geo-physical parameters that affect sensor behavior.

The CTB includes models for multiple sensor modalities, including multispectral imaging (MSI), mid-wave infrared (MWIR), long-wave infrared (LWIR), and ground penetrating radar (GPR). Other models included in the CTB are the Adaptive Hydrology (ADH) soil model and vegetation models that compute radiation transfer in plants. The ADH model calculates both surface and subsurface moisture transport through soil with thermal effects included. The vegetation model uses the fluxes generated by the Quick Caster (QC) model to compute the temperature of the vegetation. The electro-optical/infrared (EO/IR) sensor model produces synthetic infrared imagery.

Gain New Performance Insights from Realistic Models

With accurate representation of geo-environmental processes, simulations produced by CTB models have a high level of realism, offering new insights into the performance of multi-spectral imaging, mid-wave infrared, long-wave infrared, and ground penetrating radar sensors. Sensitivity analyses can be performed to identify the factors with the greatest effect on probability of detection and false alarm rates. GEOTACS may exploit this knowledge to optimize sensor performance and operations to detect threats in highly complex geo-environments.

Features

  • Computational Model Builder (CMB), including PointsBuilder, SceneBuilder and ModelBuilder components
  • ADH Soil Model
  • Vegetation Model
  • QC Model
  • Moderated Resolution Atmospheric Transmission (initially developed by the U.S. Air Force Research Laboratory)
  • MSI Sensor Model
  • MWIR Sensor Model
  • LWIR Sensor Model
  • GPR Sensor Model

Success Story

Success of this technology was demonstrated in Operation Iraqi Freedom. Specifically, this 3-D numerical modeling technology was used to evaluate the performance of infrared imaging systems and their ability to detect shallow and deeply buried targets.

This technology was also used to help the Joint Improvised Explosive Device Defeat Organization (JIEDDO) quantify the performance of multiple airborne sensor platforms to execute more informed down-selection processes. These platforms were used to detect disturbed areas of soil.

Contact

ERDCinfo@usace.army.mil