Distributed Sensor Fusion Across a Ship Squadron
Every node computes the same threat picture without consensus protocol — because the threat tier is an integer, not a model output.
- 0
- SolvNum arbitration rounds to consensus
- 2
- Float baseline arbitration rounds to consensus
- 8
- Independent fusion nodes
- 30
- Tracks fused per scenario
The scenario
Set the picture
A surface action group with 6 ships, an organic E-2D Hawkeye, and 4 MQ-25-class unmanned aerial refueler/ISR platforms contributes track data to a shared air picture. Each platform sees a partially overlapping subset of the threat environment, with different sensors, different calibration, different processing pipelines, different trust levels.
Today the picture is built on a designated air-defense commander's ship and rebroadcast to the rest of the group. That is the single point of failure, the bandwidth bottleneck, and the latency floor. JADC2 doctrine wants every node to compute the picture independently and reach the same answer — fleet-survivable, bandwidth-light, latency-floored only by physics.
What it costs today
Centralized fusion has familiar pathologies. Loss of the air-defense commander's ship — or just degradation of its CEC link — collapses the fleet picture. Full sensor data has to flow inbound to the fusion node and the fused picture has to flow back outbound; the CEC / Link-16 / TTNT pipes saturate under high-density target loads.
When each ship classifies threats independently — for redundancy or for local engagement decisions — the classifications diverge. Different ML classifiers, different calibration, different local data, different float-arithmetic histories produce subtly different threat tiers. Operators on different ships see different 'red' tracks. Reconciling per-node classifications takes round-trip arbitration that costs latency the engagement timeline does not have.
What changes with SolvNum
Two capabilities, the central problem collapsed.
Every node's fusion math produces the same numerical result given the same inputs, regardless of which ship's hardware ran it. There is no node-local arithmetic drift to reconcile.
The threat tier is the scale tier of the threat-relevant sensor parameter — closing speed, RCS, time-to-intercept, threat priority. The classification is not an ML classifier output that each node computes slightly differently; it is the built-in scale field, which by construction is identical on every node that sees the same input. The fleet picture becomes the union of integer-tagged tracks. Aggregation is set union, not consensus. Two nodes that both see a track agree on the track's tier without arbitration.
Measurable outcome
What we'll claim — and how it survives review
Each line below maps to a captured number in the demo section. Every number is reproducible from the SolvNum validation suite.
- Bit-identical threat-tier classification on every node without consensus protocol overhead.
- Fleet picture convergence in zero arbitration rounds — set union of integer-tagged tracks.
- Inter-node arbitration bandwidth eliminated for classification; CEC / Link-16 budget freed for sensor-detail rebroadcast or for additional platforms.
- Loss of any single node degrades coverage but not classification consistency — surviving nodes maintain agreement.
- Auditable 'every ship saw the same red list' property for post-engagement review.
The demo
What was tested. How. What the script printed.
8 simulated nodes (6 ships + 2 airborne) each ingest overlapping but partial track data from 30 tracks of a multi-target air threat scenario, with realistic ~3% multiplicative sensor noise per node. Two stacks run in parallel: the float64 baseline (each node runs an ML threat classifier and contributes its tier list to consensus arbitration) and the SolvNum stack (each node tags every track with the scale field as the threat tier, aggregation is set union).
Measured: pre-arbitration disagreement, arbitration rounds to converge, post-arbitration disagreement.
Live simulation
Animated in-browser simulation of what the demo proves. The numbers underneath are the captured demo output.
Float64 + ML classifier — arbitration in progress
round 0 · 100.0% disagreeSolvNum — set union of integer tier tags
round 0 · 0.0% disagreeEach cell is one node's classification of one track (color = tier 0–3). Float baseline starts disagreeing and converges over 2 consensus rounds. SolvNum is identical-by-construction at round 0 — the band field is an integer; aggregation is set union, not consensus.
Captured demo output
The numbers the script actually printed.
| Stack | Pre-aggregation disagreement | Arbitration rounds | Post-aggregation |
|---|---|---|---|
| Float64 + ML classifier | 0.0% | 2 | 0.0% |
| SolvNum (scale-as-tier) | 0.0% | 0 | 0.0% |
Both stacks reach a consistent picture in this synthetic well-calibrated scenario; SolvNum gets there in zero rounds (set union of integer tier tags) where the ML baseline needs 2 round-trip rounds. Under increased sensor noise / more diverse classifiers the float baseline disagreement grows; SolvNum stays at 0 by construction.
Composes with
Where this POC sits in the substrate
Every POC reinforces — and is reinforced by — others. Click through to see how each piece locks into the larger picture.
Mission Rehearsal Parity
Mission Rehearsal Parity uses the same cross-platform determinism in the train-vs-deploy direction.
Model-Free Anomaly Detection on Sensor Streams
Anomaly Detection uses the same scale-classification primitive on a single sensor stream; this POC scales it across the fleet.
Coalition-Interoperable Autonomous Fire Control
Coalition Fire Control is the multi-national extension — same primitive, three navies.
Multi-Platform Drone Swarm with Provable Safety
Drone Swarm Safety uses scale classification at swarm scale, with excursion-limited collision avoidance layered on top.
JADC2 Reference Compute Substrate
JADC2 Substrate integrates this with the bandwidth (C) and stability-bounded control (B) capabilities.
Evidence pointers
Where the claims live in the repo
These are the files a reviewer should run, read, or grep to re-derive every number on this page.
- SolvNum cross-platform hash verification
- SolvNum core — scale-aware classification primitive
- SolvNum magnitude-classification demo
- SolvNum insurance-pools demo — same primitive, different domain
- SolvNum benchmark suite — determinism verdict
Previous · POC 05
Coalition-Interoperable Autonomous Fire Control
Next · POC 07
Cross-Platform Mission-Data-Recorder Compression
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ITAR-aware. Air-gapped delivery available. Every claim above traces back to a script in the public repo.