Clearing the ôSea of Redö: Multi-Layered AI Approach for Scalable Battlefield Intelligence and Decision Making

Battlefield sensor systems currently face challenges in processing massive amounts of data, resulting in cluttered displays often described as a "sea of red." These systems process sensor inputs in isolation, creating inconsistent data formats and overwhelming intelligence analysts. This reduces the Armyĺs ability to quickly and accurately identify, prioritize, and target enemy forces, delaying the development of effective Courses of Action (COAs) for battlefield commanders. Senix Robotics proposes a three-layer modular Center of Mass (COM) AI-driven algorithm to solve this problem. The system automates data processing and integration to provide faster, more accurate battlefield intelligence. Layer 1 collects and processes raw sensor data using machine learning and signal processors to classify targets and identify local coordinates. Layer 2 refines and integrates this data using Kalman filters to generate precise global coordinates. Layer 3 fuses all sensor data, creating a unified battlefield view on a global map with detailed target metadata and timestamps. This modular design is customizable, allowing each layer to function independently or as part of the full system. Senix Roboticsĺ solution enhances intelligence accuracy, accelerates decision-making, and supports flexible deployment across Army operations. By standardizing intelligence workflows, this system empowers commanders with real-time insights for decisive action.