Maximum likelihood estimation of target location in acoustic and electromagnetic imaging

Schatzberg, Alon; Devaney, Anthony J.; Deming, Ross

doi:10.1190/1.1887557

Cited by 4 publications

(7 citation statements)

References 2 publications

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“…The bottom layer of the hierarchy consists of models corresponding to object categorization. Examples of DL applications to object categorization and tracking can be found in [16][17][18][19][20][21][22][23][24][25][26] . The level of activation of each model in the bottom layer can be determined based on how similar the model is to a subset of the data.…”

Section: Model Hierarchymentioning

confidence: 99%

Dynamic Logic learning in cognitive-based situation models

Ilin

Perlovsky

2011

2011 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA

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we present a cognitive modeling framework called Neural Modeling Fields (NMF) and its application to situation learning and categorization. We discuss how this framework is related to the perceptual symbol systems theory of cognition (PSS). Essentially, the mathematical apparatus of NMF is a way to learn the frames and simulators described qualitatively by PSS. For the purposes of this work, a situation is modeled as a set of objects and relationships that exist among them. Here we consider object recognition problem solved and demonstrate how the NMF framework is used to learn high level concepts such as situations.

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Section: Model Hierarchymentioning

confidence: 99%

Dynamic Logic learning in cognitive-based situation models

Ilin

Perlovsky

2011

2011 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA

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“…Using Kerns' scattering matrix formulation, a transmitting antenna centred at a position X j ≡ x j x + y j ŷ on the ground surface and driven by matched terminal voltage C(ω) will give rise to the following plane wave expansion for the incident electric field propagating in the negative ẑ-direction: [13,23]…”

Section: Forward Model For Electromagnetic Scatteringmentioning

confidence: 99%

“…Similarly, the matched terminal voltage at a receiver centred at X j (a monostatic experiment) is given by [13,23]…”

Section: Forward Model For Electromagnetic Scatteringmentioning

confidence: 99%

“…It should be emphasized that equations ( 5) and ( 6) are valid in the nearfield of the antennas, thus, we need not make the point source approximation typical to other inversion algorithms of diffraction tomography. If the transmitting and receiving antennas are reciprocal, as would be the case for a typical monostatic radar system, then [13,23]…”

Section: Forward Model For Electromagnetic Scatteringmentioning

confidence: 99%

See 1 more Smart Citation

Diffraction tomography for multi-monostatic ground penetrating radar imaging

Deming¹,

Devaney²

1997

Inverse Problems

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A generalized diffraction tomographic (DT) algorithm is derived for subsurface imaging from multifrequency multi-monostatic ground penetrating radar (GPR) data. The algorithm is based on the Born approximation for vector electromagnetic scattering and incorporates realistic nearfield models for the receiving and transmitting antennas. The forward scattering model is inverted analytically using the regularized pseudoinverse operator to yield an algorithm for imaging the underground region based on scattered field measurements at a set of receiving antennas. Whereas the usual inversion algorithms of DT require a lossless background medium and ideal point sources and receivers, the algorithm described here allows an attenuating background and arbitrary transmitting and receiving antennas. The algorithm places no restrictions on the radar frequency, and can thus include shallow imaging applications where the wavelengths are on the same order as the depth of buried objects of interest. Versions of the algorithm are given for both the three dimensional and the 2.5-dimensional cases. Results are given of computer simulations designed to test the algorithm.

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“…Our methodology is based on a version of PMHT, independently developed in [11] based on the theory of cognition and perception called Dynamic Logic (DL). This approach has been successfully applied to multiple target tracking for unresolved targets using radar, optical and acoustic sensors [12]- [19] demonstrating robust performance and showing promise in overcoming the combinatorial complexity. We will refer to our methodology as EM/DL throughout this paper.…”

Section: Introductionmentioning

confidence: 99%