An Edge-AI Driven Multi-Layered Air Defense Framework for Real-Time Cost Asymmetric Threat Prioritization

The primary contributions of this paper include developing a novel Cost-Asymmetric Threat Prioritization (CATP) algorithm that merges kinematics, trajectory alignment, and CNN-derived visual features (payload severity and destructibility) to resolve multi-target tie-breaking. Additionally, the framework introduces a Cost-Asymmetric Ratio (CAR) that achieves a 32.0% reduction in interceptor costs via a tri-layered response strategy, all while being fully validated on resource-constrained edge hardware achieving an end-to-end latency of 145.33 ms using only 3.5% CPU and 14.3% RAM.