MIROS: Elusive Unauthorized AAV Positioning by Multi-View Radar-Vision Cognitive Fusion

The proliferation of unauthorized autonomous aerial vehicles (AAVs) threatens public safety and privacy, especially elusive ones with tiny sizes, wide ranges, and unpredictable trajectories, making precise wide-range positioning challenging yet essential for threat mitigation. Existing approaches often rely on single-modal, single-view strategies, suffering severely degraded positioning error and identification accuracy from environmental adversity and limited observations. This paper presents MIROS, a distributed Multi-vIew Radar-vision cOgnitive fuSion framework architected around Observe-Orient-Decide-Act (OODA) cognitive loop for precise and robust elusive unauthorized AAV positioning. To close this loop, MIROS introduces two core innovations: (1) Orient: an abstract-binding-based fusion strategy that adaptively fuses heterogeneous sensor data via learned cross-modal and cross-view correlations; (2) Act: a situation-aware multi-view collaborative sensing strategy using dynamics modeling for proactive multi-view sensor coordination. Month-long deployments of MIROS over 3.16 × 108 m3 using minimal commodity sensors (one radar and three visual monitors) achieve 9.6 cm positioning error and improve identification accuracy by up to 2.14× over single-modal methods and 1.25× over single-view cross-modal baselines, while maintaining real-time inference and deployment scalability.