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Tag: Detectors
  • Low Size, Weight, Power, and Cost (SWaP-C) Payload for Autonomous Navigation and Mapping on an Unmanned Ground Vehicle

    Abstract: Autonomous navigation and unknown environment exploration with an unmanned ground vehicle (UGV) is extremely challenging. This report investigates a mapping and exploration solution utilizing low size, weight, power, and cost payloads. The platform presented here leverages simultaneous localization and mapping to efficiently explore unknown areas by finding navigable routes. The solution utilizes a diverse sensor payload that includes wheel encoders, 3D lidar, and red-green-blue and depth cameras. The main goal of this effort is to leverage path planning and navigation for mapping and exploration with a UGV to produce an accurate 3D map. The solution provided also leverages the Robot Operating System
  • Buried-Object-Detection Improvements Incorporating Environmental Phenomenology into Signature Physics

    Abstract: The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environmental phenomenological conditions affecting probability of detection and false alarm rates for buried-object detection using thermal infrared sensors. This study used several different approaches to identify the predominant environmental variables affecting object detection: (1) multilevel statistical modeling, (2) direct image analysis, (3) physics-based thermal modeling, and (4) application of machine learning (ML) techniques. In addition, this study developed an approach using a Canny edge methodology to identify regions of interest potentially harboring a target object. Finally, an ML method was developed to improve automatic target detection and recognition performance by accounting for environmental phenomenological conditions, improving performance by 50% over standard automatic target detection and recognition software.
  • Thermal Infra-Red Comparison Study of Buried Objects between Humid and Desert Test Beds

    Abstract: This study pertains to the thermal variations caused by buried objects and their ramifications on soil phenomenology. A multitude of environmental conditions were investigated to observe the effect on thermal infrared sensor performance and detection capabilities. Correlations between these external variables and sensor contrast metrics enable determinable key factors responsible for sensor degradation. This document consists of two parts. The first part is a summary of data collected by the U.S. Army Corps of Engineers, Engineer and Research and Development Center Cold Regions Research and Engineering Laboratory (ERDC-CRREL), ERDC-Geotechnical Structures Laboratory, and Desert Research Institute at the Yuma Proving Ground (YPG) site in February 2020 and observations from this activity. The second part is a comparison of target visibility between data collected at YPG and data collected at the ERDC-CRREL test site in 2018.
  • Spatial and Temporal Variance of Soil and Meteorological Properties Affecting Sensor Performance—Phase 2

    ABSTRACT: An approach to increasing sensor performance and detection reliability for buried objects is to better understand which physical processes are dominant under certain environmental conditions. The present effort (Phase 2) builds on our previously published prior effort (Phase 1), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried-object detection. The study utilized a 3.05 × 3.05 m test plot in Hanover, New Hampshire. Unlike Phase 1, the current effort involved removing the soil from the test plot area, homogenizing the material, then reapplying it into eight discrete layers along with buried sensors and objects representing targets of interest. Each layer was compacted to a uniform density consistent with the background undisturbed density. Homogenization greatly reduced the microscale soil temperature variability, simplifying data analysis. The Phase 2 study spanned May–November 2018. Simultaneous measurements of soil temperature and moisture (as well as air temperature and humidity, cloud cover, and incoming solar radiation) were obtained daily and recorded at 15-minute intervals and coupled with thermal infrared and electro-optical image collection at 5-minute intervals.
  • Trace Explosives Detection by Cavity Ring-Down Spectroscopy (CRDS)

    Abstract: We built three successive versions of a thermal decomposition cavity ring-down spectrometer and tested their response to explosives. These explosive compound analyzers successfully detected nitroglycerine, 2,4,6-trinitrotoluene (TNT), pentaerythryl tetranitrate, hexahydro-1,3,5-trinitro-s-triazine and triacetone triperoxide (TATP). We determined the pathlength and limits of detection for each, with the best limit of detection being 13 parts per trillion (ppt) of TNT. For most of the explosive tests, the peak height was higher than the expected value, meaning that peroxy radical chain propagation was occurring with each of the explosives and not just the peroxide TATP.
  • PUBLICATION NOTICE: Spatial and Temporal Variance in the Thermal Response of Buried Objects

    ABSTRACT:  Probability of detection and false alarm rates for current military sensor systems used for detecting buried objects are often unacceptable. One approach to increasing sensor performance and detection reliability is to better understand which physical processes are dominant under certain environmental conditions. Incorporating this understanding into detection algorithms will improve detection performance. Our approach involved studying a small, 3.05 × 3.05 m, test plot at the Engineer Research and Development Center’s Cold Regions Research and Engineering Laboratory (ERDC-CRREL) in Hanover, New Hampshire. There we monitored a number of environmental variables (soil temperature moisture, and chemistry as well as air temperature and humidity, cloud cover, and incoming solar radiation) coupled with thermal infrared and electro-optical image collection. Data collection occurred over 4 months with measurements made at 15 minute intervals. Initial findings show that significant spatial and thermal temporal variability is caused by incoming solar radiation; meteorologically driven surface heat exchange; and subsurface-soil temperatures, density, moisture content, and surface roughness.
  • PUBLICATION NOTICE: Optimized Low Size, Weight, Power and Cost (SWaP-C) Payload for Mapping Interiors and Subterranean on an Unmanned Ground Vehicle

    ABSTRACT: Section 3 of the FY15 Force 2025 Maneuvers Annual Report indicates that in Dense Urban Areas (DUA), specifically in a subsurface, surface, or super-surface structure, the ability to identify threats will be diminished. Most commercially available LIght Detection And Ranging (LIDAR) systems are specifically designed for high-resolution aerial imaging and mapping applications. As a result, they tend to be large, heavy, power-hungry, data bandwidth intensive, and expensive. They also employ lasers that are not typically eye-safe, which limits their overall effectiveness in subterranean and the interiors of subsurface or super-surface structures. However, due to recent advances in the automotive industry, there are new generations of Size, Weight, Power, and Cost (SWaP-C) sensors that are eye-safe, making them suitable for use indoors and in subterranean environments. While these tradeoffs limit their effective use to hundreds of meters (compared to kilometers for their more expensive counterparts), they are ideal candidates for use in subterranean and building interiors. While cameras fill this niche to some extent, the volumetric calculations provided by these sensors provide additional intelligence to shape the security of the environment and offer more precision when maneuvering troops. These sensors would provide the warfighter with situational understanding in previously inaccessible locations. Therefore, to aid in the Army’s need to obtain and maintain situational understanding in DUAs, the authors propose utilizing low size, weight, power, and cost (SWaP-C) sensors, on a robot platform, for surveying and mapping underground structures and building interiors. Rapid/near real-time data processing is possible by utilizing open-source software and commercial off the shelf (COTS) components. Using the preferred sensor payload autonomously was also explored.