Parallel state estimation for systems with integrated measurements

TL;DR

This paper introduces parallel Bayesian filtering and smoothing techniques for systems with integrated measurements, enabling faster state estimation by leveraging parallel computation, especially on GPUs.

Contribution

It develops novel parallel filters and smoothers for linear Gaussian SRTM models, overcoming the sequential limitation of traditional methods.

Findings

Parallel methods outperform sequential approaches in computational time.

GPU implementation demonstrates significant speedup.

Proposed algorithms maintain estimation accuracy.

Abstract

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.