Multi-Observer Multi-Spectral Passive Object Detection

Neoskye proposes an innovative multi-spectral detection and tracking system to revolutionize defense application threat detection, situational awareness, and decision-making capabilities. The system leverages advanced Transformer Networks and Reinforcement Learning (RL) algorithms to achieve real-time, high-accuracy data fusion and adaptive detection in dynamic and cluttered environments by integrating thermal, optical, and RF sensor data. The proposed solution addresses operational challenges such as adverse weather, atmospheric interference, and multi-modal clutter while ensuring robust performance and low-latency processing.The system employs edge computing frameworks, using decentralized preprocessing at sensor nodes to reduce latency to <50 ms, combined with centralized fusion pipelines achieving <150 ms end-to-end latency. Adaptive RL models trained with Proximal Policy Optimization (PPO) dynamically adjust detection parameters, ensuring >95% detection accuracy and <5% false positive rates under diverse scenarios. Integration is facilitated via REST APIs, enabling seamless compatibility with platforms like the Aegis Combat System (ACS) for real-time track management and engagement.Neoskye's innovative use of synthetic data generation with tools like COMSOL Multiphysics ensures comprehensive training and validation of algorithms across 500+ edge-case scenarios. This transformative solution enables scalable deployment across naval, ground, and aerial platforms, providing unparalleled situational awareness and operational resilience. Neoskye's approach sets new standards for multi-spectral detection, offering actionable intelligence and mission-critical support in contested environments.