An inverse scattering reconstruction method for perfect electric conductor‐dielectric hybrid target based on physics‐inspired network

Zhang, Qianqian; Yin, Chengyou; Li, Anqi; Liu, Han

doi:10.1049/rsn2.12419

Cited by 2 publications

(1 citation statement)

References 27 publications

(55 reference statements)

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“…A rough image of the zero-order T-matrix coefficients for unknown scatterers is firstly reconstructed by the back-propagation method, which is then refined by an attention-assisted pix2pix generative adversarial network. In [19] A physics-inspired neural network named APU-Net is designed to solve Inverse Scattering Problems with mixed boundary conditions. The input to the APU-Net is derived from the back-propagation method.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Mixed Boundary Objects and Classification Using Deep Learning and Linear Sampling Method

Harisha,

Mallikarjun,

Amit

2024

RADIOENGINEERING

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The linear sampling method is a simple and reliable linear inversion technique for determining the morphological features of unknown objects under investigation. Nevertheless, there are many challenges that this method depends on the frequency of operation and it is unable to produce satisfactory results for objects with complex shapes. This paper proposes a hybrid model, which combines conventional linear sampling method and deep learning for the reconstruction of mixed boundary objects. In this approach, the initial approximation of mixed boundary objects derived from linear sampling method serves as the training data for the U-Net based convolutional neural network. The network then learns to correlate this approximation with the corresponding ground truth profiles. Along with the reconstruction of mixed boundary objects, they are also classified as dielectric or conductor, and count of each object type are measured. Furthermore, the low-frequency and highfrequency characteristics of the linear sampling method are analyzed, and its limitations are overcome by combining it with a deep learning approach. The effectiveness of the proposed model is validated using several examples of synthetic and experimental data. The results demonstrate that the proposed method outperforms the conventional Linear sampling method in terms of accuracy.

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Section: Introductionmentioning

confidence: 99%

Reconstruction of Mixed Boundary Objects and Classification Using Deep Learning and Linear Sampling Method

Harisha,

Mallikarjun,

Amit

2024

RADIOENGINEERING

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Inverse design of a novel multiport power divider based on hybrid neural network

Sun,

Zhu,

et al. 2024

Concurrency and Computation

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SummaryIn this study, we propose an inverse design approach based on a neural network for a novel multiport power divider (MP‐PD) with complex geometry. The inverse design approach is obtaining geometry from the desired physical performance to address the challenge of conventional methods. We develop a hybrid neural network model for this inverse design. The backbone architecture incorporates a bidirectional long short‐term memory module, a multihead self‐attention module, and convolutional modules. This hybrid neural network is employed to capture the feature of physical performance and learn the relationship between the geometric structure of the proposed MP‐PD and its corresponding physical performance. Consider the design of the power divider as an end‐to‐end methodology that directly maps design requirements to optimal geometric parameters. The neural network transfers the designed process into multiple‐input‐multiple‐output. We adopt the network model to successfully predict 20 geometric parameters of MP‐PDs for two distinct operating frequencies. The two operating frequencies are those utilized in real engineering applications, which are 3.5 GHz in the 5G band and 2.45 GHz in the trackside communication band. The predicted MP‐PD improves the return loss and bandwidth by 8.05 dB and 0.25 GHz, respectively, over the desired performance. The experiments and comparisons demonstrate the effectiveness and accuracy of our inverse design approach. The efficiency and flexibility of design are also significantly improved by the hybrid neural network model.

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An inverse scattering reconstruction method for perfect electric conductor‐dielectric hybrid target based on physics‐inspired network

Cited by 2 publications

References 27 publications

Reconstruction of Mixed Boundary Objects and Classification Using Deep Learning and Linear Sampling Method

Reconstruction of Mixed Boundary Objects and Classification Using Deep Learning and Linear Sampling Method

Inverse design of a novel multiport power divider based on hybrid neural network

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