Efficient delay-tolerant particle filtering

TL;DR

This paper introduces a computationally efficient delay-tolerant particle filtering framework that selectively processes measurements based on informativeness, reducing computational load with minimal impact on tracking accuracy.

Contribution

It presents a novel framework that estimates measurement informativeness and optimizes threshold selection through a constrained problem, improving efficiency in particle filtering.

Findings

Processes less than 40% of OOSMs with minimal accuracy loss

Uses a lightweight procedure for measurement informativeness estimation

Optimizes threshold to balance accuracy and computational cost

Abstract

This paper proposes a novel framework for delay-tolerant particle filtering that is computationally efficient and has limited memory requirements. Within this framework the informativeness of a delayed (out-of-sequence) measurement (OOSM) is estimated using a lightweight procedure and uninformative measurements are immediately discarded. The framework requires the identification of a threshold that separates informative from uninformative; this threshold selection task is formulated as a constrained optimization problem, where the goal is to minimize tracking error whilst controlling the computational requirements. We develop an algorithm that provides an approximate solution for the optimization problem. Simulation experiments provide an example where the proposed framework processes less than 40% of all OOSMs with only a small reduction in tracking accuracy.