Sequential feature selection for detecting buried objects using forward looking ground penetrating radar

Stone, Kevin; Ho, K. C.; Keller, James M.; Luke, Robert H.; Burns, Brian

doi:10.1117/12.2224272

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…Several studies based on the identification of targets type through the scattered signal including underwater fish species recognition [ 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ] or objects identification in the surrounding environment (in air, water, etc) [ 59 ] use time-domain statistical characteristics, Wavelets, matching pursuit methods or features correlated with the energy or shape of echoes [ 60 ]. Considering the lack of studies involving the analysis of the back-scattered signal provided from a trapped particle acquired through the optical fiber, the features explored here were selected based on previous studies about object recognition through scattering signals acquired using photodetectors or other kind of “event counter” equipment.…”

Section: Methodsmentioning

confidence: 99%

Single Particle Differentiation through 2D Optical Fiber Trapping and Back-Scattered Signal Statistical Analysis: An Exploratory Approach

Paiva

Ribeiro

Cunha

et al. 2018

Sensors

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Recent trends on microbiology point out the urge to develop optical micro-tools with multifunctionalities such as simultaneous manipulation and sensing. Considering that miniaturization has been recognized as one of the most important paradigms of emerging sensing biotechnologies, optical fiber tools, including Optical Fiber Tweezers (OFTs), are suitable candidates for developing multifunctional small sensors for Medicine and Biology. OFTs are flexible and versatile optotools based on fibers with one extremity patterned to form a micro-lens. These are able to focus laser beams and exert forces onto microparticles strong enough (piconewtons) to trap and manipulate them. In this paper, through an exploratory analysis of a 45 features set, including time and frequency-domain parameters of the back-scattered signal of particles trapped by a polymeric lens, we created a novel single feature able to differentiate synthetic particles (PMMA and Polystyrene) from living yeasts cells. This single statistical feature can be useful for the development of label-free hybrid optical fiber sensors with applications in infectious diseases detection or cells sorting. It can also contribute, by revealing the most significant information that can be extracted from the scattered signal, to the development of a simpler method for particles characterization (in terms of composition, heterogeneity degree) than existent technologies.

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Section: Methodsmentioning

confidence: 99%

Single Particle Differentiation through 2D Optical Fiber Trapping and Back-Scattered Signal Statistical Analysis: An Exploratory Approach

Paiva

Ribeiro

Cunha

et al. 2018

Sensors

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show abstract

“…Active systems such as GPR have been coupled to a thermal camera's superior standoff capabilities to complement GPR's higher detection performance (Malof and Collins 2016;Hendrickx et al 2006). As a standalone sensor modality, GPR has been gaining popularity in landmine detection (Shaw et al 2016;Kelly et al 2019). The polarization effects on GPR have been studied extensively Le 2016, Sagnard 2017), as have the effects from soil moisture (Miller et al 2004;Miller et al 2002).…”

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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“…Active systems, such as GPR coupled to a thermal camera's superior standoff capabilities, complement GPR's higher detection performance (Malof and Collins 2016;Hendrickx et al 2006). As a standalone sensor modality, GPR has been gaining popularity in subsurface object detection (Shaw et al 2016;Kelly et al 2019). Studies have extensively explored the polarization effects on GPR (Dogaru and Le 2016;Sagnard 2017) and the effects from soil moisture (Miller et al 2002(Miller et al , 2004.…”

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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Sequential feature selection for detecting buried objects using forward looking ground penetrating radar

Cited by 7 publications

References 4 publications

Single Particle Differentiation through 2D Optical Fiber Trapping and Back-Scattered Signal Statistical Analysis: An Exploratory Approach

Single Particle Differentiation through 2D Optical Fiber Trapping and Back-Scattered Signal Statistical Analysis: An Exploratory Approach

Modernizing environmental signature physics for target detection—Phase 3

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

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