Deep Sigma Point Processes for RCS Modeling in Spaceborne SAR Imagery

• URL: http://arxiv.org/abs/2607.21745v1
• Date: Thu, 23 Jul 2026 18:54:28 GMT
• Title: Deep Sigma Point Processes for RCS Modeling in Spaceborne SAR Imagery
• Abstract summary: Radar cross-section (RCS) modeling is foundational to advancing the utility and sensitivity of spaceborne radar systems.<n>This study introduces a deep sigma-point process (DSPP) model for predicting RCS in synthetic aperture radar (SAR) imagery.
• Score: 3.3815414420459002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• ProSR: Semantic-Prototype-Guided Discrete Modeling for Physically Consistent SAR Super-Resolution [28.150128853779268]
  High-resolution Synthetic Aperture Radar (SAR) imagery is critical for precision analysis as automatic target recognition, yet its acquisition is costly.<n>We propose Prototype-Guided Super-Resolution (ProSR) to reformulating SAR ISR as a semantically-guided discrete token prediction task.<n>ProSR achieves superior visual quality while preserving essential scattering characteristics required for practical SAR applications.
  arXiv  Detail & Related papers  (2026-09-02T09:50:06Z)
• Covariance-Boosted Gaussian Processes for Spatiotemporal Irregularities [0.0]
  Motivated by ionospheric modeling for satellite-based augmentation systems, this paper proposes a Covariance-Boosted Gaussian Process (CBGP) framework.<n>CBGP model efficacy and robustness are demonstrated through out-of-sample testing of both simulated and real-world applications.<n>The modeling of an extensive ionospheric storm dataset over South America suggests accurate and reliable means to compute SBAS ionospheric corrections.
  arXiv  Detail & Related papers  (2026-07-25T03:24:04Z)
• Von Mises Based Uncertainty Quantification for Closely Spaced Automotive Radar Targets [0.5378499388029053]
  This work investigates uncertainty-aware deep learning approaches for direction of arrival (DOA) estimation in automotive radar.<n>A circular-based von Mises (VM) ensemble (ENS) is compared with an evidential deep learning (EDL) framework based on a normal inverse gamma formulation.<n>ENS achieves lower uncertainty under nominal conditions and exhibits stronger sensitivity to severe perturbations, whereas EDL provides smoother uncertainty variation and slightly improved ranking consistency.
  arXiv  Detail & Related papers  (2026-06-30T10:49:45Z)
• Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty [5.844591976842085]
  We develop an end-to-end framework that combines noise pre-estimation, constrained likelihood optimization, and prediction calibration in one pipeline.<n>We extend the framework to sub-Gaussian settings via optional Gaussianization and provide closed-form standard errors through a block-structured Fisher analysis.
  arXiv  Detail & Related papers  (2026-05-12T06:38:12Z)
• Neural Posterior Estimation of Terrain Parameters from Radar Sounder Data [49.62628745057007]
  We propose a simulation-based inference approach to terrain parameter inversion from radar sounder data.<n>Synthetic observations from a GPU-based simulator are used to train a neural network-based density estimator for neural posterior estimation.<n>We demonstrate that our NPE model is well calibrated on simulated data and transferable to real Mars radar profiles.
  arXiv  Detail & Related papers  (2026-05-05T08:41:02Z)
• On the Characterization and Limits of 4D Radar for Aided Inertial Navigation [11.648973329789973]
  Noise models are derived by applying first principles to the underlying signal processing of a typical radar sensor.<n>The approach is first examined through simulation, evaluating the significance of different noise sources.<n>Experiments demonstrate the superior robustness and accuracy of the proposed method across diverse field environments and flight profiles.
  arXiv  Detail & Related papers  (2026-05-03T08:12:54Z)
• Function-Space Decoupled Diffusion for Forward and Inverse Modeling in Carbon Capture and Storage [65.51149575007149]
  We present Fun-DDPS, a generative framework that combines function-space diffusion models with differentiable neural operator surrogates for both forward and inverse modeling.<n>Fun-DDPS produces physically consistent realizations free from the high-frequency artifacts observed in joint-state baselines.
  arXiv  Detail & Related papers  (2026-02-12T18:58:12Z)
• On the Plasticity and Stability for Post-Training Large Language Models [54.757672540381236]
  We identify a root cause as the conflict between plasticity and stability gradients.<n>We propose Probabilistic Conflict Resolution (PCR), a framework that models gradients as random variables.<n>PCR significantly smooths the training trajectory and achieves superior performance in various reasoning tasks.
  arXiv  Detail & Related papers  (2026-02-06T07:31:26Z)
• ProAct: Agentic Lookahead in Interactive Environments [56.50613398808361]
  ProAct is a framework that enables agents to internalize accurate lookahead reasoning through a two-stage training paradigm.<n>We introduce Grounded LookAhead Distillation (GLAD), where the agent undergoes supervised fine-tuning on trajectories derived from environment-based search.<n>We also propose the Monte-Carlo Critic (MC-Critic), a plug-and-play auxiliary value estimator designed to enhance policy-gradient algorithms.
  arXiv  Detail & Related papers  (2026-02-05T05:45:16Z)
• Controllable Probabilistic Forecasting with Stochastic Decomposition Layers [1.3995263206621]
  We introduce Decomposition Layers (SDL) for converting deterministic machine learning weather models into ensemble systems.<n>SDL applies learned perturbations at three decoder scales through latent-driven modulation, per-pixel noise, and channel scaling.<n>When applied to WXFormer via transfer learning, SDL requires less than 2% of the computational cost needed to train the baseline model.
  arXiv  Detail & Related papers  (2025-12-21T17:10:00Z)
• ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving [64.42138266293202]
  ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
  arXiv  Detail & Related papers  (2025-10-09T17:59:36Z)
• RadProPoser: A Framework for Human Pose Estimation with Uncertainty Quantification from Raw Radar Data [1.5318029014836756]
  We introduce RadProPoser, a probabilistic encoder-decoder architecture that processes complex-valued radar tensors.<n>By incorporating variational inference into keypoint regression, RadProPoser jointly predicts 26 three-dimensional joint locations.<n>On our newly released dataset with optical motion-capture ground truth, RadProPoser achieves an overall mean per-joint position error (MPJPE) of 6.425 cm, with 5.678 cm at the 45 degree angle.
  arXiv  Detail & Related papers  (2025-08-05T15:46:05Z)
• Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences [118.91584633024907]
  A novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems.
  arXiv  Detail & Related papers  (2023-04-29T00:39:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.