VT-MRF-SPF: Variable Target Markov Random Field Scalable Particle Filter

TL;DR

This paper introduces VT-MRF-SPF, an online particle filtering algorithm for high-dimensional spatiotemporal Markov random fields, effectively addressing challenges like partial observations and dynamic dimensions in complex tracking scenarios.

Contribution

The paper presents a novel scalable particle filter for high-dimensional STMRFs that overcomes the curse of dimensionality and includes practical tuning guidelines.

Findings

Demonstrates superior performance in large-scale experiments

Guarantees algorithm's effectiveness in high-dimensional settings

Provides practical guidelines for parameter tuning

Abstract

Markov random fields (MRFs) are invaluable tools across diverse fields, and spatiotemporal MRFs (STMRFs) amplify their effectiveness by integrating spatial and temporal dimensions. However, modeling spatiotemporal data introduces additional hurdles, including dynamic spatial dimensions and partial observations, prevalent in scenarios like disease spread analysis and environmental monitoring. Tracking high-dimensional targets with complex spatiotemporal interactions over extended periods poses significant challenges in accuracy, efficiency, and computational feasibility. To tackle these obstacles, we introduce the variable target MRF scalable particle filter (VT-MRF-SPF), a fully online learning algorithm designed for high-dimensional target tracking over STMRFs with varying dimensions under partial observation. We rigorously guarantee algorithm performance, explicitly indicating overcoming the curse of dimensionality. Additionally, we provide practical guidelines for tuning graphical parameters, leading to superior performance in extensive examinations.