Improved Gaussian Process Regression Inspired by Physical Optics for the Conducting Target's RCS Prediction

Xiao, Donghai; Guo, Lixin; Liu, Wei; Hou, Muyu

doi:10.1109/lawp.2020.3034169

Cited by 9 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. The RMSE and the cost time of the missile model were also calculated using following methods: The SVR model optimized by particle swarm optimizer (PSO); the SVR model without any optimizer; the backward propagation (BP) neural network [5][6][7]; the Gaussian process regression (GPR) model [8]; the polynomial chaos expansion (PCE) [17], and the low rank approximation (LRA) [18].…”

Section: A Mono-rcs Of a Missile Modelmentioning

confidence: 99%

“…Researchers have proposed ML models for EM solver design [2], repairing damaged receivers' data [3], and low scattering meta-surface design [4], etc. ML has also been applied in RCS prediction [5][6][7][8][9][10], but the existing techniques still have some limitations. For instance, 8326 samples are required for a single frequency point in [5], which may not be applicable for computationally expensive EM problems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wideband Monostatic RCS Prediction of Complex Objects using Support Vector Regression and Grey-wolf Optimizer

Zhang,

Wang,

2024

ACES Journal

View full text Add to dashboard Cite

This paper presents a method based on the support vector regression (SVR) model and grey wolf optimizer (GWO) algorithm to efficiently predict the monostatic radar cross-section (mono-RCS) of complex objects over a wide angular range and frequency band. Using only a small-size of the mono-RCS data as the training set to construct the SVR model, the proposed method can predict accurate mono-RCS of complex objects under arbitrary incident angle over the entire three-dimensional space. In addition, the wideband prediction capability of the method is significantly enhanced by incorporating the meta-heuristic algorithm GWO. Numerical experiments verify the efficiency and accuracy of the proposed SVR-GWO model over a wide frequency band.

show abstract

Section: A Mono-rcs Of a Missile Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wideband Monostatic RCS Prediction of Complex Objects using Support Vector Regression and Grey-wolf Optimizer

Zhang,

Wang,

2024

ACES Journal

View full text Add to dashboard Cite

show abstract

“…GPR is known as a supervised machine learning technique that successfully addresses regression and classification issues, providing advantages such as effective performance with small datasets and the provision of uncertainty metrics for predictions [38]. Essentially, GPR operates as nonparametric kernel-based probabilistic models, offering a general-purpose solution for supervised learning in regression and probabilistic classification [43], [44].…”

Section: ) Gaussian Process Regression (Gpr)mentioning

confidence: 99%

Machine Learning-Optimized Compact Frequency Reconfigurable Antenna With RSSI Enhancement for Long-Range Applications

Yahya,

Soeung,

Rahim

et al. 2024

IEEE Access

View full text Add to dashboard Cite

This study presents an innovative and compact monopole antenna with dual-band frequency reconfigurability for LoRa applications. It operates within the 915 MHz and 868 MHz frequencies, aligning with the designated bands for use in America, Asia and Europe. No existing compact reconfigurable antenna with these features for LoRa applications within ISM bands below 1 GHz is known. Employing an economical FR-4 substrate in its design, the antenna attains a compact size of 40 × 42 mm 2 (0.12 λ 0 × 0.12 λ 0 ), where λ 0 denotes the wavelength in free space corresponding to 868 MHz. A single RF PIN diode enables seamless switching between 868 MHz and 915 MHz bands. Design, simulation, and optimization employed CST MWS ® software. Supervised regression Machine Learning (ML) models predicted resonance frequencies, with Gaussian Process Regression emerging as optimal, achieving Rsquared and variance scores of 92.87% and 93.77%, respectively. A maximum gain of 2 dBi at 915 MHz and 70% efficiency, boasting good radiation patterns and matching was demonstrated by the antenna. Experimental validation in a football field at Universiti Teknologi PETRONAS, Malaysia, assessed the proposed antenna's performance on a LoRa transceiver system based on LoRa SX1276. The Received Signal Strength Indicator (RSSI) of the proposed antenna consistently exceeded the conventional commercially available monopole antenna by an average of −12 dBm at every point up to 300 m, showcasing enhanced signal reception. The antenna proves promising for wireless sensor nodes in long-range applications.INDEX TERMS Dual-band, LoRa, PIN-diode, reconfigurable, RSSI.

show abstract

Efficient RCS Extrapolation Method of Target Scaling Based on Physical Optics

Chen,

Zhao,

2023

2023 International Conference on Microwave and Millimeter Wave Technology (ICMMT)

View full text Add to dashboard Cite

Improved Gaussian Process Regression Inspired by Physical Optics for the Conducting Target's RCS Prediction

Cited by 9 publications

References 12 publications

Wideband Monostatic RCS Prediction of Complex Objects using Support Vector Regression and Grey-wolf Optimizer

Wideband Monostatic RCS Prediction of Complex Objects using Support Vector Regression and Grey-wolf Optimizer

Machine Learning-Optimized Compact Frequency Reconfigurable Antenna With RSSI Enhancement for Long-Range Applications

Efficient RCS Extrapolation Method of Target Scaling Based on Physical Optics

Contact Info

Product

Resources

About