Unification of Fusion Theories, Rules, Filters, Image Fusion and Target Tracking Methods (UFT)

The author has pledged in various papers, conference or seminar presentations, and scientific grant applications (between 2004-2015) for the unification of fusion theories, combinations of fusion rules, image fusion procedures, filter algorithms, and target tracking methods for more accurate applications to our real world problems - since neither fusion theory nor fusion rule fully satisfy all needed applications. For each particular application, one selects the most appropriate fusion space and fusion model, then the fusion rules, and the algorithms of implementation. He has worked in the Unification of the Fusion Theories (UFT), which looks like a cooking recipe, better one could say like a logical chart for a computer programmer, but one does not see another method to comprise/unify all things. The unification scenario presented herein, which is now in an incipient form, should periodically be updated incorporating new discoveries from the fusion and engineering research.

💡 Research Summary

The paper presents a comprehensive blueprint for unifying the disparate strands of fusion research—fusion theories, combination rules, image‑fusion procedures, filtering algorithms, and target‑tracking methods—into a single, adaptable framework. Recognizing that no single theory (Bayesian probability, Dempster‑Shafer evidence, fuzzy sets, possibility theory, etc.) or rule (simple averaging, weighted averaging, Dempster‑Shafer combination, PCR5, Yager‑Kohler, etc.) can satisfy every real‑world application, the author proposes a step‑wise “recipe” that guides practitioners from problem definition to implementation.

The first step is the selection of an appropriate fusion space and underlying mathematical model based on the nature of uncertainty in the data. Probabilistic models are recommended when independence assumptions hold, Dempster‑Shafer when evidence conflict is prominent, and fuzzy or possibilistic models when linguistic vagueness dominates. Each model is evaluated on criteria such as confidence function, conflict‑resolution capability, and computational load.

Next, the framework introduces a hierarchical rule‑selection mechanism. By quantifying data correlation, confidence distribution, and real‑time constraints through explicit metrics (conflict degree, information gain, etc.), the system can automatically decide whether to apply a single rule, a cascade of rules, or a parallel combination. This flexibility reduces information loss and mitigates contradictory evidence.

The third layer integrates filtering and image‑fusion techniques. Classical state‑estimation filters (Kalman, extended Kalman, particle) are coupled with multi‑scale image pyramids, wavelet transforms, or deep‑learning‑based feature extractors. This hybridization simultaneously suppresses spatio‑temporal noise and enhances high‑resolution details, while remaining modular so that individual filters or image‑processing blocks can be swapped without redesigning the whole system.

The fourth component addresses target tracking. The fused data and filtered estimates feed into multi‑hypothesis tracking (MHT) or Bayesian network trackers, allowing seamless incorporation of heterogeneous sensors (radar, optical, infrared). The resulting tracker benefits from the enriched information content and reduced uncertainty provided by the preceding fusion stages.

To make the approach practical for developers, the author supplies a visual flowchart and a series of tables that enumerate all selectable options at each stage, effectively acting as a logical chart or cookbook for programmers. Standardized APIs and data formats are defined, enabling plug‑in style extensions. Consequently, when new uncertainty models, fusion rules, or deep‑learning‑based techniques emerge, they can be integrated with minimal effort, ensuring the framework remains future‑proof.

Finally, the paper emphasizes an open, modular architecture that supports continuous updates. By encouraging community‑driven repositories and shared plug‑ins, the framework aims to evolve alongside advances in fusion theory and engineering practice. In sum, the work offers a pragmatic, scalable roadmap that bridges theory and application, reduces development cost, and enhances system reliability across a wide spectrum of fusion‑dependent domains.