Real-Time Data Processing Framework for Combat-Ready Situational Intelligence using Integrated Sensor Networks
Deepak Sahu, Nisha Pandey, Rahul Kumar Verma, Ram Kaji Budhathoki · 2026
The speed, precision, and dependability of real-time situational awareness technology are critical to prudent decision-making in contemporary combat operations. Legacy sensor architectures respond more slowly and expose units to operational risk from disparate data streams, latency, and sparse cross-platform compatibility. All these combine to make it increasingly difficult to sense threats, recognize targets, and coordinate missions particularly in adversarial and ambiguous operational environments. An end-to-end sensor fusion and real-time data processing architecture for high-speed, high-fidelity battlefield intelligence is presented. A distributed thermal, acoustic, LIDAR, and radar sensor mesh is used in the presented system. These inputs are combined through a real-time edge computing engine driven by low-latency neural inference and graph-based analytics. Field units, command posts, and autonomous platforms all have simultaneous data flow with a hierarchical communications system. 1.1-second processing latency, situational awareness accuracy of 97.2%, sensor fusion integrity of 94.8%, and threat detection precision of 96.5% in ambient noise and electronic interference were all proven in field simulations in contested areas. These findings verify the system&s;s ability to provide effective, synchronized, and useful intelligence in real-time, greatly improving operational superiority and combat readiness.