Multimodal Sensor Fusion

Definition

Multimodal Sensor Fusion is fusion of data across different sensing modalities, including imagery, RF, acoustic, cyber, text, and tracks. In defense applications, it helps detect concealed, moving, or spoofed targets by combining independent evidence streams. The hard part is misaligned modalities and hidden correlation between sources, especially when systems are deployed across contested links, coalition boundaries, and mixed human-machine teams. KhanBMS treats it as a KhanBMS evidence graph rather than a single sensor truth, tying the concept back to modular command, edge execution, and auditable authority.

Reference attributes

Layer

multimodal perception layer

Operational value

Helps detect concealed, moving, or spoofed targets by combining independent evidence streams

Primary risk

Misaligned modalities and hidden correlation between sources

KhanBMS role

A KhanBMS evidence graph rather than a single sensor truth

Related terms

• AI Sensor Fusion
  Machine-learning methods that combine multiple sensor streams into a better estimate than any source alone.
• Multimodal Foundation Models (MFM)
  Foundation models that jointly process text, imagery, video, audio, maps, and structured sensor data.
• AI Data Fabric
  Integrated data layer that connects operational, sensor, model, metadata, and governance sources for AI workflows.
• Cooperative Perception
  Shared perception where multiple platforms combine local observations to improve detection and tracking.