Influence Diagrams for Robust Multi-Target Tracking
Priyank Behera, C. Robert Kenley

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…
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Taxonomy
TopicsTarget Tracking and Data Fusion in Sensor Networks · Distributed Sensor Networks and Detection Algorithms · Gaussian Processes and Bayesian Inference
