Fusion & Decision Engine

The multi-INT fusion platform that goes beyond tracks on a map.

Most intelligence fusion stops at the track - correlate the radar and the RF, draw a dot, call it a picture. Real multi-INT fusion runs the whole stack: from raw signal, to object, to the situation those objects form, to what it means, to what you should do about it. Empyrean fuses across every source - sensors, identity and finance, environment, narrative, and space - through all six levels of the recognized data-fusion model, unclassified and deployable to the edge. Sensor fusion is Level 1. This is the other five.

The Problem

Sensors produce fragmented, overlapping, and sometimes conflicting data. Greedy association picks the best match per detection independently, which means the first detection processed gets the best track and the last gets whatever is left. In a dense environment with dozens of targets and multiple sensor types at wildly different accuracies and update rates, order-dependent matching produces cascading mis-associations. Add spoofing, jamming, and sensor dropout to the mix and most fusion systems fall apart. Operators end up reconciling phantom tracks instead of making decisions. But a track is not a decision. Knowing where 30 objects are tells you nothing about whose they are, what they form, what they intend, or what you should do before the window closes. Most "fusion" tools resolve objects and hand the operator a denser map - more dots, same questions. The harder problem is everything above the track: correlating the object to its identity and its owner, assembling objects into a situation, projecting the situation into consequence, and turning that into an action a team at the edge can take without reaching back to a classified enclave they may not have. That is the gap. The big platforms sell the top of this stack as a national-tier, classified capability. The team that actually has to act rarely has it.

Who Has This Problem

Special operations and tactical teams fusing organic and attached sensorsAir support and tactical air control elementsLaw-enforcement tactical and fusion unitsInteragency and joint interagency task forcesSearch-and-rescue and public-safety incident commandMaritime and port security and the Coast GuardCritical-infrastructure and site securityInsurers and commercial risk teams pricing real-world exposureSystem integrators wiring multi-source architecturesAnyone who needs the whole picture without a SCIF to build it in
Intelligence fusion with active overlays from weather, environmental, air domain, space sensors, and contributing domains
Fusion detail panel showing drone on approach with multi-sensor corroboration and identity provenance
Course of Action generator with physics-backed firing solutions, blast damage modeling, proportional navigation, and engagement geometry

Key Workflows

01Correlate across dozens of heterogeneous sensors per zone: radar, RF, EO/IR, acoustic, cooperative broadcasts, passive detection, and more. Sensor-agnostic. Protocol-agnostic. Optimal global assignment so the 25th detection gets the same quality assignment as the first.
02Multi-hypothesis tracking with independent kinematic state per hypothesis. When targets cluster and separate, tracks follow their target smoothly instead of jumping 200m on resolution. Zone-scoped trust policies escalate automatically under spoofing.
03Situation assessment that resolves the identity and ownership behind a track. Select an AIS vessel or ADS-B aircraft and the platform resolves it through its registry record, ownership chain, and sanctions exposure - tying the physical object to who controls it.
04Threat projection that runs an anomaly or a course of action through the physics-backed simulation engine. Model what happens if the threat continues on heading, or what happens if you commit an interceptor now versus in 30 seconds.
05Policy-driven automated response at Level 4: the Policy Engine evaluates compound conditions across all domains and fires doctrine-as-flows - escalating trust posture, retasking sensors, alerting the watch floor, or dispatching effectors within authority gates.
06Analyst-driven pivot and decision support at Level 5: the investigation workbench, the entity relationship graph, and course-of-action tools that keep the human in command of consequential decisions with the full fused picture behind them.
07Cross-domain correlation across all five INT families: a radar track correlated with its RF signature, its registered owner, the weather degrading the sensor that sees it, the narrative campaign mentioning it, and the satellite that will overfly it in twelve minutes.
08Merge, split, and coast lifecycle with full lineage. Velocity-scaled coast so a slow UAS does not drop track during a 70-second radar gap while a cruise missile at Mach 3 does not coast into the next grid square.
09Spoof detection with automatic escalation: duplicate identity, kinematic conflict, replay, false emitter. Spoof alerts can auto-escalate the zone's trust posture without operator intervention.
10Edge-deployed, air-gapped, cold-startable. The full fusion stack - from Level 0 ingest through Level 5 decision support - running on a single node at the tactical edge without a cloud dependency.

Capabilities

Full JDL/DFIG fusion stack from Level 0 (source ingest) through Level 5 (human decision support) - not just Level 1 track fusion
All-source fusion across five INT families: sensor, identity/financial, environmental, narrative/OSINT, and space
4-state identity trust lifecycle (unverified, corroborated, degraded, revoked) with automatic degradation when confirming sensors go stale
Corroboration-gated track initiation: tentative contacts require multi-sensor confirmation in contested mode before promotion to the fused picture
Multi-hypothesis tracking with optimal global assignment - not greedy, not order-dependent
6-state filtering with terrain-aware confidence adjustment and 3D altitude gating for co-located targets at different flight levels
Situation assessment and cross-domain correlation tying objects to identity, ownership, and network membership
Entity and ownership resolution feeding sanctions and beneficial-ownership findings back into the track detail panel
Anomaly detection and threat scoring with physics-backed impact projection via the simulation engine
Policy-driven process refinement - the Policy Engine closing the loop with doctrine-as-flows and automated sensor tasking
Provenance-aware identity resolution (claimed, observed, inferred) with quality scaling from sensor metadata
Simulation-backed course-of-action support - the same fusion engine exercisable in wargame mode before contact
Validated against 60+ object contested airspace scenarios with 10+ heterogeneous sensor types
Edge-deployed, air-gappable, cold-startable - the entire stack running at the tactical edge without cloud dependency

Platform Integration

Fusion is the spine of the platform - the Make Sense layer between Sense (the sensor and source domains) and Act (the Policy Engine, the decision layer, and the agency relationships that carry action authority). Every domain feeds it: EMSO emitter tracks, SSA satellite passes, Narrative Intelligence campaign signals, Digital Force Protection anomalies, Threat Finance & Entity Resolution ownership findings, and environmental confidence modifiers from WEIS. Every domain consumes its output: the COP renders fused tracks with provenance, the Policy Engine evaluates compound conditions across all fused data, and the simulation engine stresses the same pipeline with synthetic scenarios. Fusion is not a feature of the platform. It is the platform.

← All capabilities

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Empyrean Defense

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