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Sensor fusion combines signals from multiple sensors into a single, more accurate physical estimate, while data fusion combines multi-source data, including sensor output, into decision-ready understanding of a situation.
Sensor fusion combines signals from multiple sensors into a single, more accurate physical estimate (a track, a position, a detection), while data fusion combines multi-source data, including sensor output, into decision-ready understanding of a situation. Sensor fusion answers "what is out there"; data fusion answers "what does it mean for the mission."
The two are often used interchangeably, but for intelligence analysis the distinction matters: most analytic value is created above the sensor layer, where records, reports, and sensor tracks have to be reconciled into one coherent picture.
Sensor fusion merges measurements from several sensors of the same or different types to produce an estimate that is more accurate, more complete, or more reliable than any single sensor could provide. A classic case is combining radar, EO/IR, and RF to hold one confident track on a moving object when any one sensor alone would drop it. It operates mostly on numeric, time-synchronized signals close to the platform.
Data fusion combines information from many sources - sensors, structured records, unstructured reports, open-source material - into an aligned, enriched representation an analyst or system can act on. It preserves context, resolves which records refer to the same real-world entity, and makes relationships explicit. The long-standing reference model for these levels of processing is the JDL Data Fusion Model (Joint Directors of Laboratories) [verify primary source before publish], which separates object, situation, and impact assessment.
| Sensor fusion | Data fusion | |
|---|---|---|
| Inputs | Multiple sensor signals | Multi-source: sensors, records, reports, OSINT |
| Question answered | What is out there? (track, position) | What does it mean? (situation, relationships) |
| Data type | Numeric, time-synchronized | Mixed - structured and unstructured |
| Output | A refined physical estimate | Decision-ready, connected understanding |
| Where it runs | At/near the platform | Across enterprise and mission systems |
Sensor fusion is typically a component that feeds data fusion. In an all-source or multi-INT workflow, refined sensor tracks are one input among many that data fusion has to reconcile with everything else known about an entity.
Multi-INT fusion is data fusion applied across intelligence disciplines - correlating what different collection sources report about the same entity or event. This is where fragmented, multi-source data most often increases decision risk: the same entity appears under different identifiers across systems, and the relationships between entities are implicit. Resolving identity and making relationships explicit is the hard part, and it is upstream of any dashboard or model.
Torch.AI treats fusion as reasoning infrastructure that sits across existing systems: NEXUS makes multi-source data meaningful (semantic representation), and HALO makes relationships understandable (graph-based fusion and reasoning), so multi-source inputs become connected, traceable, mission-ready understanding without replacing the systems that already hold the data. This matters for programs pursuing CJADC2-style data integration, where the goal is shared understanding across services rather than another isolated tool [cite: Open DAGIR / JADC2 reference material - verify link].
Sensor fusion merges measurements from several sensors into a single estimate more accurate or reliable than any one sensor alone.
Data fusion combines multi-source data, including sensor output, into an aligned, enriched representation an analyst or system can act on.
Sensor fusion answers what is out there from sensor signals; data fusion answers what it means by combining sensors with records, reports, and open sources into decision-ready understanding.
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