Influence Diagrams for Robust Multi-Target Tracking

TL;DR

This paper introduces an influence diagram-based approach to improve multi-target tracking accuracy in environments with correlated measurement noise, outperforming classical methods like JPDAF in simulations.

Contribution

It presents a novel influence diagram framework for JPDAF, addressing limitations of classical Kalman filtering under colored noise conditions.

Findings

ID-JPDAF achieves lower RMSE than classical methods

Simulation results validate improved tracking accuracy

Influence diagrams enhance robustness in correlated noise environments

Abstract

Multi-Target Tracking (MTT) is foundational for radar, defense, and autonomous systems, where tracking accuracy directly affects decision-making and safety. For linear systems with Gaussian process and measurement noise, the Kalman filter remains the gold standard for state estimation. However, its performance can degrade in real-world scenarios where measurement noise is temporally correlated. This violates the white-noise assumptions that Kalman filters have. Various approaches include state augmentation of the Kalman filter, but this approach is susceptible to failure due to ill-conditioned problem formulations. This work investigates the limitations of classical Kalman filtering in colored noise environments and presents an influence diagram-based approach to the Joint Probabilistic Data Association Filter (JPDAF). Simulation results on benchmark scenarios demonstrate that the Influence Diagram JPDAF (ID-JPDAF) achieves lower root mean square error (RMSE) than classical methods. These findings highlight the potential of influence diagram models for advancing multi-target tracking performance in radar and related applications.