用于状态估计的动态在线处理器原生推理
Dynamic Online Processor-Native Inference for State Estimation
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中文总结 AI 辅助
针对贝叶斯方法中似然性计算瓶颈，提出用处理器原生不确定性跟踪的贝叶斯滤波技术，经原生操作实现确定性分层重要性重构。实验表明该技术能实现确定性近似滤波，在模型评估和后验推理上有优势，且在均方根误差方面与基线粒子滤波器有竞争力。
中文摘要 AI 辅助
传感器丰富的数据驱动应用越来越多地使用贝叶斯方法从噪声传感器测量和物理模型推断动态系统的潜在状态。然而，似然性的计算仍然是准确后验和高性能推理的关键瓶颈。本文提出一种贝叶斯滤波技术,利用处理器原生不确定性跟踪进行不确定性传播和推理。该技术通过原生操作实现确定性分层重要性重构，为以程序代码编写的任意模型提供确定性延迟和有限内存使用。在三个非线性状态空间系统上的基准测试将该方法与粒子滤波器和基于蒙特卡洛的似然估计器进行比较。该技术能够进行确定性近似滤波，在模型评估中，与直接蒙特卡洛工作相比，平均加速比高达805倍，结果质量匹配；在进行后验推理时，能实现帕累托主导的精度-延迟权衡，同时在均方根误差方面与基线粒子滤波器具有竞争力。
英文摘要
Sensor-rich data-driven applications increasingly use Bayesian approaches to infer latent states of dynamic systems from noisy sensor measurements and physical models. Yet the computation of the likelihood remains an essential bottleneck for accurate posteriors and performant inference. This paper presents a Bayesian filtering technique that uses processor-native uncertainty tracking for both uncertainty propagation and inference. The technique implements deterministic hierarchical importance restructuring through a native operation, giving deterministic latency and bounded memory use for arbitrary models written as program code. Benchmarks across three nonlinear state-space systems compare the approach against particle filters and Monte-Carlo-based likelihood estimators. The technique enables deterministic approximate filtering with as high as 805$\times$ average speedup against direct Monte Carlo work at matched result quality for model evaluation, and Pareto-dominant accuracy-latency trade-offs for posterior inference while remaining competitive in RMSE with baseline particle filters.
