Acoustic Impedance and Its Application in Seismo-Acoustic Landmines Detection Models

Li, Hongyu; Wang, Yijia; Chen, Hongwei; Wang, Xiao; Yu, Zhihao; Guo, Sumin

doi:10.2112/si99-014.1

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Yao's team used an IR lamp adjusted to 800 W/m 2 to mimic the natural diurnal cycle of Earth in a controlled indoor environment. Li et al (2020) took a different approach by observing how buried objects impacted the acoustic-to-seismic coupling efficiency by tracking the changes in acoustic impedance. Similarly, von Deimling et al…”

Section: Background Environmental Phenomenological Impacts On Thermal...mentioning

confidence: 99%

Modernizing environmental signature physics for target detection—Phase 3

Clausen¹,

Felt²,

Musty³

et al. 2022

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The present effort (Phase 3) builds on our previously published prior efforts (Phases 1 and 2), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried object detection. Environmental phenomenological effects are often represented in weather forecasts in a relatively coarse, hourly resolution, which introduces concerns such as exclusion or misrepresentation of ephemera or lags in timing when using this data as an input for the Army’s Tactical Assault Kit software system. Additionally, the direct application of observed temperature data with weather model data may not be the best approach because metadata associated with the observations are not included. As a result, there is a need to explore mathematical methods such as Bayesian statistics to incorporate observations into models. To better address this concern, the initial analysis in Phase 2 data is expanded in this report to include (1) multivariate analyses for detecting objects in soil, (2) a moving box analysis of object visibility with alternative methods for converting FLIR radiance values to thermal temperature values, (3) a calibrated thermal model of soil temperature using thermal IR imagery, and (4) a simple classifier method for automating buried object detection.

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Section: Background Environmental Phenomenological Impacts On Thermal...mentioning

confidence: 99%

Modernizing environmental signature physics for target detection—Phase 3

Clausen¹,

Felt²,

Musty³

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Yao's team used an IR lamp adjusted to 800 W/m 2 to mimic the natural diurnal cycle of Earth in a controlled indoor environment. Li et al (2020) took a different approach by looking at acoustic impedance to observe how buried objects impacted the acoustic-to-seismic coupling efficiency.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal variance of soil and meteorological properties affecting sensor performance—Phase 2

Clausen¹,

Frankenstein²,

Dorvee³

et al. 2021

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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 inter-est. 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.

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Section: Multisensor Fusion Including Thermal Irmentioning

confidence: 99%

Buried-object-detection improvements incorporating environmental phenomenology into signature physics

Clausen¹,

Truong²,

Bragdon³

et al. 2022

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The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environ-mental 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.

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Acoustic Impedance and Its Application in Seismo-Acoustic Landmines Detection Models

Cited by 5 publications

References 18 publications

Modernizing environmental signature physics for target detection—Phase 3

Modernizing environmental signature physics for target detection—Phase 3

Spatial and temporal variance of soil and meteorological properties affecting sensor performance—Phase 2

Buried-object-detection improvements incorporating environmental phenomenology into signature physics

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