Soil moisture affects soil thermal and dielectric properties and may cause false alarms in detecting manmade objects when dielectric or thermal discontinuities exist in the soil. The spatial variability of soil moisture changes with time and it is important to understand this behavior because it is relevant for detection of small targets, and for modeling background moisture and temperature. Surface moisture of the top 6 cm of soil was sampled on regular grids with an impedance probe at a 0.1-m interval during wetting and drying events, both four days in duration. Maximum variances for data collected in August 2004 increased with decreasing mean moisture, as soil dried following a soaking rainfall. Maximum variances in June 2005 decreased over several days of intermittent rain as the soil rewetted following a prolonged drought. Spatially dependent ranges of approximately 0.5-m lag distance and exponential model fits were consistent among all the data sets, despite changes in moisture, moisture trend, and sample variance. The procession of spatial variation is described by variograms that transition from high to low maximum variances (sills) for wetting events, and from low to high maximum variances for drying events. A linear relationship between the maximum variance and mean of square root of ε was consistent for both years, except when the soil was incompletely wetted after a drought. The highest spatial variance in moisture that produced the most variable background for small target detection occurred as a consequence of the incomplete or uneven wetting following a drought.
The USA Engineer Research and Development Center (ERDC) has conducted on-/off-road experimental field testing with full-sized and scale-model military vehicles for more than fifty years. Some 4000 acres of local terrain are available for tailored field evaluations or verification/validation of future robotic designs in a variety of climatic regimes. Field testing and data collection procedures, as well as techniques for quantifying terrain in engineering terms, have been developed and refined into algorithms and models for predicting vehicle-terrain interactions and resulting forces or speeds of military-sized vehicles. Based on recent experiments with Matilda, Talon, and Pacbot, these predictive capabilities appear to be relevant to most robotic systems currently in development. Utilization of current testing capabilities with sensor-based vehicle drivers, or use of the procedures for terrain quantification from sensor data, would immediately apply some fifty years of historical knowledge to the development, refinement, and implementation of future robotic systems. Additionally, translation of sensor-collected terrain data into engineering terms would allow assessment of robotic performance a priori deployment of the actual system and ensure maximum system performance in the theater of operation.
This paper examines the attribution of data fields required to generate high resolution soil profiles for support of Computational Test Bed (CTB) used for countermine research. The countermine computational test bed is designed to realistically simulate the geo-environment to support the evaluation of sensors used to locate unexploded ordnance. The goal of the CTB is to derive expected moisture, chemical compounds, and measure heat migration over time, from which we expect to optimize sensor performance. Several tests areas were considered for the collection of soils data to populate the CTB. Collection of bulk soil properties has inherent spatial resolution limits. Novel techniques are therefore required to populate a high resolution model. This paper presents correlations between spatial variability in texture as related to hydraulic permeability and heat transfer properties of the soil. The extracted physical properties are used to exercise models providing a signature of subsurface media and support the simulation of detection by various sensors of buried and surface ordnance.
This paper outlines an approach to extrude two-dimensional existing information to support the prediction of subsurface discrete objects. The goal of this study is to develop one part of a toolkit to generate realistic, simulated subsurface material and property distributions supporting detection of surface and subsurface explosives. A sample one-meter cubic grid of heterogeneous media is simulated along with expected deviations. We explain the method used to generate a volume of soil, number of geologic features included (if any), and location. This method is expected to be used to depict a larger surface area based on representative limited visual and laboratory data. The soil volume will be used to exercise models providing a signature of subsurface media allowing the simulation of detection by various sensors of buried and surface ordnance.
The focus of this study is the evaluation of emerging soil moisture models as they apply to infrared, radar, and acoustic sensors within the scope of countermine operations. Physical, chemical, and biological processes changing the signature of the ground are considered. The available models were not run in-house, but were evaluated by the theory by which they were constructed and the supporting documentation. The study was conducted between September and October of 2003 and represents a subset of existing models. The objective was to identify those models suited for simulation, define the general constraints of the models, and summarize the emerging functionalities which would support sensor modeling for mine detection.