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Research on a Prediction Model Based on a Newton–Raphson-Optimization–XGBoost Algorithm Predicting Environmental Electromagnetic Effects for an Airborne Synthetic Aperture Radar

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Publicado en:Electronics vol. 14, no. 11 (2025), p. 2202
Autor principal: Shen, Yan
Otros Autores: Chen, Yazhou, Wang, Yuming, Ma, Liyun, Zhang, Xiaolu
Publicado:
MDPI AG
Materias:
Standard deviation
Test systems
Signal to noise ratio
Prediction models
Synthetic aperture radar
Optimization
Signal processing
Electromagnetic interference
Antennas
Newton-Raphson method
Image degradation
Algorithms
Performance degradation
Image quality
Parameters
Airborne radar
Distance learning
Reconnaissance
Information warfare
Acceso en línea:Citation/Abstract
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Resumen:Airborne synthetic aperture radar (SAR) serves as critical battlefield reconnaissance equipment, yet it remains vulnerable to electromagnetic interference (EMI) in combat environments, leading to image-quality degradation. To address this challenge, this study proposes an EMI-effect prediction framework for airborne SAR electromagnetic environments, based on the Newton–Raphson-based optimization (NRBO) and XGBoost algorithms. The methodology enables interference-level prediction through electromagnetic signal parameters obtained from reconnaissance operations, providing operational foundations with which SAR systems can mitigate the impacts of EMI. A laboratory-based airborne SAR EMI test system was developed to establish mapping relationships between EMI signal parameters and SAR imaging performance degradation. This experimental platform facilitated EMI-effect investigations across diverse interference scenarios. An evaluation methodology for SAR image degradation caused by EMI was formulated, revealing the characteristic influence patterns of different interference signals in the context of SAR imagery. The NRBO–XGBoost framework was established through algorithmic integration of Newton–Raphson search principles with trap avoidance mechanisms from the Newton–Raphson optimization algorithm, optimizing the XGBoost hyperparameters. Utilizing the developed test system, comprehensive EMI datasets were constructed under varied interference conditions. Comparative experiments demonstrated the NRBO–XGBoost model’s superior accuracy and generalization performance relative to conventional prediction approaches.
ISSN:2079-9292
DOI:10.3390/electronics14112202
Fuente:Advanced Technologies & Aerospace Database