UniFusion: Unified Data Fusion

• UniFusion is a comprehensive framework unifying evidence, spatial-temporal, and multimodal fusion under mathematically principled models.
• It employs unified fusion rules that adaptively blend classical operators, filter algorithms, and neutrosophic methods to handle diverse data and uncertainties.
• Its advanced generative models and transformer architectures facilitate cross-modal synthesis and robust inference in applications like autonomous perception and medical imaging.

UniFusion refers to a set of contemporary frameworks, techniques, and theoretical formalisms that unify disparate data fusion paradigms—encompassing evidence theories, spatial-temporal information integration, multi-modal feature alignment, and cross-modal generative modeling—under mathematically principled or algorithmically coherent models. The central motivation is to enable adaptive, scalable, and theoretically grounded fusion across heterogeneous domains, such as sensor data, image modalities, temporal sequences, and vision-language tasks, providing general solutions to real-world problems that require robust reasoning under uncertainty, multi-source feature aggregation, and joint inference.

1. Theoretical Foundations: Unification of Fusion Spaces and Rules

A core advance underlying the UniFusion family is the formal extension of classic fusion spaces. Traditional fusion theories—including Dempster–Shafer, Bayesian probability, Yager’s rule, Transferable Belief Model (TBM), Dubois-Prade’s rule, and Dezert–Smarandache Theory (DSmT)—typically define their frame of discernment over the power set or hyper-power set, with closure under unions and sometimes intersections. The UFT framework generalizes this by introducing the super-power set, a Boolean algebra closed under union, intersection, and complement. This allows for explicit representation and combination of non-exclusive, non-exhaustive, and even paraconsistent hypothesis structures (Smarandache, 2015).

A paradigmatic formula of this generalized belief assignment is:

$$MUFT(A) + MUFT(B) + MUFT(A \cup B) + MUFT(A \cap B) + \ldots = 1,$$

with %%%%1%%%% terms for $n$ atomic elements, encompassing all possible unions, intersections, and complements.

Fusion rules in UniFusion are synthesized to dynamically select or blend standard operators:

• Conjunctive rule: for all-reliable sources.
• Disjunctive rule: when some sources are unreliable.
• Exclusive/disjunctive rule: if only one source is presumed correct.
• Mixed rules: for hybrid evidence environments.

A unified fusion rule is exemplified by:

$$MUFR(A) = \sum_{X_1, X_2 \in S_0, X_1 * X_2 = A} d(X_1*X_2) \cdot T(X_1,X_2) \cdot \frac{P(A)/Q(A)}{P(A)/Q(A)+P(X)/Q(X)},$$

where $*$ is the set operation, $d$ a degree coefficient, $T$ a (generalized) norm (fuzzy or neutrosophic), and $P, Q$ encode proportional parameters.

2. Unified Filter Algorithms and Fusion for Target Tracking

Beyond theoretical unification, UniFusion frameworks extend to the fusion of dynamic processes via a modular filter selection and integration system, termed "Unification of Filter Algorithms" (UFA). This approach abstracts over:

• Classical Kalman Filter (KF)
• Extended Kalman Filter (EKF)
• Unscented Kalman Filter (UKF)
• Particle filters (PF)
• Alpha–Beta, Alpha–Beta–Gamma, Daum, Wiener filters

In operation, UFA examines the system’s current regime (e.g., linear Gaussian, nonlinear, multi-modal noise) and adaptively selects the most appropriate filter—potentially mixing outputs—or monitors performance via the normalized innovation squared (NIS) metric to switch adaptively (Smarandache, 2015).

For target tracking, non-linear recurrences (generalized Fibonacci-type) are also incorporated for modeling non-linear state evolution, allowing improved filter performance in real-world, non-linear dynamical scenes.

3. Image Fusion, Multimodal Alignment, and Scene Understanding

In image fusion and multi-modal perception, UniFusion frameworks formalize the use of:

• Fuzzy logic operators: T-norms (conjunction), T-conorms (disjunction).
• Neutrosophic operators: N-norms, N-conorms, with each pixel or feature represented as triplets $P_{NS}(T, I, F)$ expressing truth, indeterminacy, and falsehood.

These are instantiated for image denoising, segmentation, and edge detection. For example, transformation to the neutrosophic domain uses

$$T(i, j) = \frac{g(i, j) - g_{min}}{g_{max} - g_{min}}, \quad F(i, j) = 1 - T(i, j),$$

with $I(i, j)$ calculated as the normalized absolute deviation from the local mean.

Emerging UniFusion models such as UniFuse (Su et al., 28 Jun 2025) for medical imaging extend this principle by coupling alignment, restoration, and fusion—introducing modules such as Degradation-Aware Prompt Learning and Omni Unified Feature Representation. These use multi-directional feature modeling (Spatial Mamba blocks) and curvature-adaptive fusion (ALSN with LoRA-inspired scaling) to achieve robust, all-in-one fusion of degraded, misaligned, and heterogeneous medical images.

In continuous scene mapping, Uni-Fusion (Yuan et al., 2023) introduces a universal kernel-based encoder for arbitrary surface properties. By dividing space into voxels and representing each with a compact latent feature—learned implicitly from kernel approximations—Uni-Fusion supports real-time incremental reconstruction, transfer of fabricated surface properties (e.g., 2D saliency or style to 3D), and open-vocabulary scene understanding via CLIP feature fields, without requiring direct training on each new modality or property.

4. Unified Spatial–Temporal Fusion for Autonomous Perception

In spatiotemporal contexts, such as Bird’s-Eye-View (BEV) mapping for autonomous vehicles, UniFusion (Qin et al., 2022) realizes a unified transformer-based architecture that fuses multi-camera spatial features and temporal context. The model defines:

$$F_{BE} = \mathbb{F}_{\text{spatial}}(F_{\text{img}}) + \mathbb{F}_{\text{temporal}}(F_{\text{past}})$$

where each component is handled by transformer modules extracting spatial and temporal context using self-attention. The distinctiveness lies in:

• Learnable fusion weights for temporal adaptation, as opposed to fixed weights of earlier BEV approaches.
• End-to-end optimization of both spatial and temporal fusion modules, leading to refined context aggregation, especially for dynamic and occluded scenarios.
• Capability for long-range temporal fusion and improved robustness against scene transitions and sensor misalignments.

This results in consistent state-of-the-art performance on map segmentation benchmarks such as NuScenes.

5. Unified Generative and Foundational Modeling

In generative vision-LLMing, modern UniFusion systems (Li et al., 14 Oct 2025) leverage frozen large vision-LLMs (VLMs) as unified multimodal encoders for diffusion-based generative models (such as DiT). Using Layerwise Attention Pooling (LAP), UniFusion aggregates multi-layer VLM features—capturing both low-level visual detail and high-level semantic abstraction. These layer-aggregated encodings condition the diffusion process, and a refining Transformer module mitigates position bias from causal auto-regressive models.

The model introduces VLM-Enabled Rewriting Injection with Flexible Inference (Verifi), where prompts are re-written by the VLM to generate new, more contextually precise conditioning tokens. The approach supports:

• Stronger text-image alignment and prompt adherence for both synthesis and editing tasks.
• Zero-shot generalization to multi-reference cases.
• Efficient cross-modal transfer and fusion of knowledge.

Empirical comparisons show improved VQA alignment and editing capabilities compared to competitive unified encoder models, even with more limited training data.

6. Applications and Impact

UniFusion frameworks enable a wide spectrum of applications:

Domain	Key Roles	Impact
Data fusion	Unification of rules/theories, uncertainty	More robust evidence integration
Target tracking	Adaptive filter selection/nonlinear mixing	Higher tracking accuracy, efficiency
Image analysis	Joint alignment/denoising/fusion	Superior quality under degradation
Robotics/SLAM	Continuous mapping (geometry/properties)	Real-time, multi-property fielding
Autonomous cars	Spatial-temporal fused perception (BEV)	Improved trajectory/scene analysis
Gen. modeling	Unified encoder for VLM-conditioned DiT	Cross-modal synthesis, editing

Applications span multisensor integration in air-force and safety systems, real-time medical imaging, high-resolution mapping, open-vocabulary semantic scene parsing, and foundation vision models for multi-modal sensor streams.

7. Outlook and Future Research

UniFusion frameworks are actively evolving, with several future directions delineated:

• Deeper mathematical paper of non-linear recurrences in filtering and fusion.
• Enhanced neutrosophic and fuzzy operator design for feature space generalization.
• Scale-up and integration of diverse filter families and learning-based approaches in dynamic scenes.
• Automatic parameter selection for proportional mass redistribution in unified fusion rules.
• Expansion into new modalities (audio, depth, multi-spectral), and extension to multi-modal decision making, safety systems, and complex sensor networks.
• Refinement of rewriting and refinement modules in VLM-conditioned diffusion for semantic precision.

A plausible implication is that unification strategies in fusion—not limited to traditional sensor fusion, but extending to foundational models, generative tasks, and multimodal inference—will continue to lower the barriers between still-siloed research subfields, ultimately driving more efficient, adaptive, and generalizable intelligent systems.