用概率模型提升卫星雷达对船体反射强度的预测精度与可信度
Deep Sigma Point Processes for RCS Modeling in Spaceborne SAR Imagery
- 基于贝叶斯高斯过程构建分层不确定性模型，融合雷达、船舶与环境多维特征
- 相比线性回归，误差降低20.83%，决定系数提升25.89%，残差更稳定
- 可输出置信区间，适合需要可靠判断的遥感监测与决策系统
雷达散射截面（RCS）建模是提升星载雷达系统效能与灵敏度的基础。本研究提出一种深度σ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采用分层高斯过程框架与贝叶斯推断，捕捉雷达信号、船舶参数及环境条件之间复杂关系的内在不确定性。与依赖确定性方程和静态参数的传统方法不同，该模型生成预测分布而非单一估计值，更全面反映雷达回波动态。通过带自动相关性判定的Matern核函数，模型识别并排序雷达、运行与环境域的关键特征，增强可解释性。性能评估显示，相比线性回归基线，测试集上均方根误差降低20.83%，决定系数提升25.89%，残差四分位距与中位绝对偏差均下降44.4%。通过提供校准的不确定性边界，DSPP显著提升预测可靠性，支持鲁棒决策。该工作推动从固定方程向结果分布的范式转变，深化对RCS行为的理解，使系统在动态环境中更具适应性。
原文摘要 · Abstract (English)
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.
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