让粒子导航更稳健：通过全局信息优化粒子运动方向
Population-Aware Physics-Informed Neural Particle Flow for Robust Spacecraft Bayesian Navigation
- 用深度集合模型捕捉所有粒子的全局状态，指导个体运动
- 在100次无线定位和100次测距任务中显著提升后验分布估计精度
- 无需真实样本或标注轨迹，适合复杂航天器导航场景
航天器导航常需从稀疏非线性观测中进行贝叶斯推断，此类问题常导致弯曲、多模态或几何受限的后验分布。物理信息神经粒子流（PINPF）通过学习从先验到后验的确定性映射场来应对，但其粒子级架构未显式考虑粒子群体的统计特性。本文提出群体感知的PINPF（PA-PINPF），将每个粒子的速度条件于整个粒子集的置换不变深度集合表示。PA-PINPF-State 捕捉粒子位置整体分布，而 PA-PINPF-Feature 提取局部物理信息（如似然与得分）。二者均保留无监督的PINPF残差项，且无需后验样本或标注传输轨迹。方法在100个随机化时差到达任务（模拟射频交叉链路与地面站定位）及100个带机载传感器视场约束的测距任务上评估，结果表明群体级贝叶斯特征能为非线性航天器导航中的粒子迁移提供有效全局信息。
原文摘要 · Abstract (English)
Spacecraft navigation often requires Bayesian inference from sparse nonlinear measurements that produce curved, multimodal, or geometrically constrained posterior distributions. Physics-informed neural particle flow (PINPF) addresses such problems by learning a deterministic prior-to-posterior transport field from the governing probability evolution equation, but its particle-wise architecture does not explicitly account for the empirical particle population. This paper introduces population-aware PINPF (PA-PINPF), which conditions each particle velocity on a permutation-invariant Deep Sets representation of the complete particle set. PA-PINPF-State summarizes particle positions, whereas PA-PINPF-Feature summarizes the local physics-informed features, including likelihood and score information. Both retain the unsupervised PINPF residual and require neither posterior samples nor labeled transport trajectories. The methods are evaluated over 100 randomized time-difference-of-arrival tasks representative of RF cross-link and ground-station localization and 100 range-measurement tasks with an onboard sensor field-of-view constraint. Results demonstrate that population-level Bayesian features provide useful global information for learned particle transport in nonlinear spacecraft-navigation problems.
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