RCS Simulations and Measurements of an Autonomous Spherical RCS Calibration Device

Wallentine, Spencer; Jost, R. J.; Reddy, C. J.; Cannon, Joel M.

doi:10.23919/amta55213.2022.9954994

Cited by 3 publications

(1 citation statement)

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“…This increases the difficulty of applying this method to a low-RCS optimization process for electrically large targets. During the low-RCS optimization process of electrically large-sized targets, a significant amount of time is still required to continuously adjust the target model and simulate and calculate its RCS value [15,16]. When accelerating the electromagnetic simulation calculation of electrically large-sized targets with the help of neural networks, although previous studies provide feasible methods such as EM-FCNN, the neural network structure used in the low-RCS optimization process is still a general FCNN [12].…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Electromagnetic Scattering Computation Acceleration Using LSTM Neural Networks

Yang,

Xinyang,

Wang

et al. 2023

Electronics

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This paper presents the electromagnetic long short-term memory neural network (EM-LSTMNN) approach to accelerate radar cross-section (RCS) calculations in optimizing low RCS for electrically large targets. The proposed method converts the conventional electromagnetic numerical calculation into an efficient numerical calculation using the LSTM neural network, resulting in a significant improvement in RCS computation speed. To assess the effectiveness of this approach, a downscaled model of a large-sized ship is employed as the target for low RCS optimization. Each modification made to the target’s mesh data during the optimization process is stored in the dataset as a new element. As the ship scaling model undergoes modifications during the optimization process, the new mesh data are recorded, thus adding a new element to the dataset at each time step. This forms a time series representation of the mesh model. By utilizing the dataset collected throughout the optimization process, the proposed EM-LSTMNN model is trained using data-driven approaches, with a training time of approximately 106 s. It is worth noting that this training time is significantly smaller compared to existing methods that employ fully connected neural networks. The performance of the proposed approach is demonstrated by comparing the RCS calculation results obtained through this method with those obtained through traditional electromagnetic simulations.

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Section: Introductionmentioning

confidence: 99%