发表机构
Defence Research and Development Canada; Memorial University of Newfoundland（加拿大国防研究与发展部; 纽芬兰纪念大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究利用深度西格玛点过程（DSPP）模型预测星载合成孔径雷达图像中的雷达散射截面（RCS），该模型采用分层高斯过程框架及贝叶斯推理，能表征不确定性，识别关键特征，性能优于线性回归基线，提高了预测可靠性，推动向概率模型转变。
AI 中文摘要
雷达散射截面（RCS）建模对于提高星载雷达系统的效用和灵敏度至关重要。本研究引入深度西格玛点过程（DSPP）模型，利用包含208,191艘已验证船舶的RADARSAT - 2数据集预测合成孔径雷达（SAR）图像中的RCS。DSPP模型不仅追求预测准确性，还能表征雷达信号、船舶参数和环境条件之间复杂关系中固有的不确定性。它采用具有贝叶斯推理的分层高斯过程框架，通过生成预测分布而非单一估计来考虑雷达回波的复杂动态。使用带有自动相关性确定的Matern核，DSPP识别并对关键特征进行排序，支持透明度和可解释性。性能评估表明该模型优于线性回归基线，在测试数据上均方根误差降低20.83%，R平方增加25.89%，残差四分位距和中位数绝对偏差降低44.4%。通过提供校准的不确定性界限，DSPP提高了预测可靠性并支持稳健决策。这项工作代表了向纳入复杂现象固有不确定性的概率模型的转变，有助于更深入理解RCS行为并使系统在动态环境中有效运行。
英文摘要
Radar cross-section (RCS) modeling is foundational to advancing the utility and sensitivity of spaceborne radar systems. This study introduces a deep sigma-point process (DSPP) model for predicting RCS in synthetic aperture radar (SAR) imagery using a RADARSAT-2 dataset containing 208,191 verified ships. The DSPP model not only strives for predictive accuracy but also characterizes the uncertainty inherent in the intricate relationships among radar signals, ship parameters, and environmental conditions. Unlike traditional approaches that rely on deterministic equations with static parameters, the DSPP uses a hierarchical Gaussian process framework with Bayesian inference to capture variability and uncertainty in RCS predictions. By generating predictive distributions rather than single estimates, the model accounts for the complex dynamics governing radar returns. Using a Matern kernel with automatic relevance determination, the DSPP identifies and ranks critical features across radar, operational, and environmental domains, thereby supporting transparency and interpretability. Performance evaluations demonstrate the model's superiority over linear regression baselines, with a 20.83 percent reduction in root mean squared error, a 25.89 percent increase in R-squared, and a 44.4 percent reduction in both the residual interquartile range and median absolute deviation on the test data. By providing calibrated uncertainty bounds, the DSPP enhances prediction reliability and supports robust decision-making. This work represents a shift toward probabilistic models that incorporate the inherent uncertainty of complex phenomena. By transitioning from fixed equations to distributions over outcomes, the DSPP fosters a deeper understanding of RCS behavior and enables systems to operate effectively in dynamic environments.
