This paper presents parallel-in-time state estimation methods for systems with Slow-Rate inTegrated Measurements (SRTM). Integrated measurements are common in various applications, and they appear in analysis of data resulting from processes that require material collection or integration over the sampling period. Current state estimation methods for SRTM are inherently sequential, preventing temporal parallelization in their standard form. This paper proposes parallel Bayesian filters and smoothers for linear Gaussian SRTM models. For that purpose, we develop a novel smoother for SRTM models and develop parallel-in-time filters and smoother for them using an associative scan-based parallel formulation. Empirical experiments ran on a GPU demonstrate the superior time complexity of the proposed methods over traditional sequential approaches.

翻译：本文针对具有慢速率集成测量（SRTM）的系统，提出了并行时间状态估计方法。集成测量在各类应用中普遍存在，常见于需要对采样周期内的物质进行收集或积分处理的数据分析过程中。现有SRTM状态估计方法本质上具有顺序性，其标准形式无法实现时间维度的并行化。本文针对线性高斯SRTM模型提出了并行贝叶斯滤波器与平滑器。为此，我们首先为SRTM模型设计了一种新型平滑器，进而基于关联扫描的并行化框架，开发了对应的并行时间滤波器与平滑器。在GPU上进行的实证实验表明，所提方法相较于传统顺序方法具有更优的时间复杂度。