High-Frequency Scattering From Radially Uniaxial Dielectric Sphere

Yazdani, Mohsen; Mautz, Joseph R.; Murphy, Luke; Arvas, E.

doi:10.1109/lawp.2015.2413399

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…On the other hand, the analysis of multilayered spheres in terms of obtaining computational forms for their scattered fields has recently been discussed in detail [6]. When the scattering from spherical targets is extended to multilayered conditions, the variety of possibilities motivated further research studies on obtaining solutions for more challenging cases such as uniaxial dielectric spheres [14], radially [15] and/or rotationally symmetric inhomogeneous anisotropic spheres [16].…”

Section: Related Workmentioning

confidence: 99%

Utilizing resonant scattering signal characteristics via deep learning for improved classification of complex targets

Toprak¹,

Selver²,

Seçmen³

et al. 2021

Turk J Elec Eng & Comp Sci

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Object classification using late-time resonant scattering electromagnetic signals is a significant problem found in different areas of application. Due to their unique properties, spherical objects play an essential role in this field both as a challenging target and a resource of analytical late-time resonant scattering electromagnetic signals. Although many studies focus on their detailed analysis, the challenges associated with target classification by resonant late-time resonant scattering electromagnetic signals from multilayer spheres have not been investigated in detail. Moreover, existing studies made the simplifying assumption that the objects having (one or more) layers constitute equal permeability values at the core and coatings. However, especially for metamaterials, magneto-dielectric inclusions require consideration of magnetic properties as well as dielectric ones. In this respect, this study shows that the utilization late-time resonant scattering electromagnetic signals of magnetic spheres provide diverse information and features, which result in superior object classification performance. For this purpose, first, time-domain late-time resonant scattering electromagnetic signals are generated numerically for single and multilayer radially symmetrical dielectric and magnetic spheres. Then, by using emerging deep learning tools, particularly convolutional neural networks trained with spheres having different material properties, a high multilayer object classification performance is achieved. Furthermore, by incorporating the frequency characteristics of the late-time resonant scattering electromagnetic signals to the classification process through Fourier transform and convolutional neural network layers for feature extraction, a convolutional neural network with long short term memory algorithm is developed. The outcome of the proposed algorithm design is shown to be particularly successful even in the case of limited available data on challenging targets. This extended strategy is also shown to outperform modern data augmentation and transfer learning techniques in terms of accuracy as well as the computational cost.

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Section: Related Workmentioning

confidence: 99%

Utilizing resonant scattering signal characteristics via deep learning for improved classification of complex targets

Toprak¹,

Selver²,

Seçmen³

et al. 2021

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…where X ∈ C B×n T is the downlink pilot contributing at both radar and communication modes and Y ∈ C B×n R is the uplink pilot which contributes only in communication mode. Thus, (14) becomes…”

Section: Pilot Sequence Designmentioning

confidence: 99%

“…Given the ever-increasing demand for both high-speed data services and accurate remote sensing capabilities, modern wireless systems will increasingly require more efficient strategies for use of the available frequency spectrum [5]- [9]. In particular, the coexistence of communication and radar systems has recently attracted a significant research interest [10]- [14]. For example, different schemes for coexisting communication and radar systems has been proposed; see e.g.…”

Section: Introductionmentioning

confidence: 99%

ComSens: Exploiting pilot diversity for pervasive integration of communication and sensing in MIMO-TDD-Frameworks

Mousaei

Soltanalian

Smida

2017

MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)

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Abstract-In this paper, we propose a fully-integrated radar and communication system -named ComSens. We utilize two different pilot sequences (one for uplink and one for downlink) with the condition that they must be uncorrelated to each other. Within such a framework, the signal received from end-user and the backscattered signal from the desired objects have uncorrelated pilots. Thus, the base-station is able to distinguish data signal from user and back-scattered signal from object. We assume a time division duplex (TDD) framework. The pilot sequences are designed for MIMO channels. We evaluate channel MSE as a figure of merit for communication system. We also show that the designed pilots are uncorrelated for a range of time lags. Moreover, designed uplink pilot has negligable autocorrelation for a range of time lags leading to an impulse-like autocorrelation for radar sensing.

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Utilizing Resonant Scattering Signal Characteristics of Magnetic Spheres via Deep Learning for Improved Target Classification

Selver

Toprak

Seçmen

et al. 2019

2019 International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

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High-Frequency Scattering From Radially Uniaxial Dielectric Sphere

Cited by 5 publications

References 18 publications

Utilizing resonant scattering signal characteristics via deep learning for improved classification of complex targets

Utilizing resonant scattering signal characteristics via deep learning for improved classification of complex targets

ComSens: Exploiting pilot diversity for pervasive integration of communication and sensing in MIMO-TDD-Frameworks

Utilizing Resonant Scattering Signal Characteristics of Magnetic Spheres via Deep Learning for Improved Target Classification

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