{"banner":"USEIF Multi-Modal Sensor Fusion Engine","version":"1.1.0","status":"ACTIVE","activeFusedTracks":242,"totalTracksCreated":4966,"totalObservationsIngested":119332,"correlationsPerformed":6750,"successfulCorrelations":1784,"correlationRate":"26.4%","sensorTypesActive":["AIS","RF"],"sensorContributionBreakdown":{"AIS":324},"fusionCycleCount":135,"lastFusionCycle":"2026-10-28T01:44:20.165Z","startedAt":"2026-10-28T01:10:30.514Z","config":{"correlationDistanceM":5000,"correlationTimeWindowS":60,"sensorWeights":{"RF":0.35,"ACOUSTIC":0.2,"RADAR":0.3,"EO_IR":0.25,"SIGINT":0.3,"HUMINT":0.1},"minimumCorrelationScore":0.3,"trackCoastTimeS":60,"trackDropTimeS":180},"multiModalProof":{"description":"The fusion engine correlates observations from ANY sensor type — RF, acoustic, radar, EO/IR, SIGINT, HUMINT — into unified fused tracks. Each track carries weighted confidence from all contributing sensors. More sensor types = higher confidence = better decisions.","sensorWeights":{"RF":0.35,"ACOUSTIC":0.2,"RADAR":0.3,"EO_IR":0.25,"SIGINT":0.3,"HUMINT":0.1},"correlationMethod":"Position proximity (Haversine) + time window + category match + IFF agreement + multi-modal diversity bonus","positionFusion":"Weighted centroid — each sensor's position weighted by (confidence * sensor_weight). CEP improves by 1/sqrt(N) with N independent sensors."}}