Mars Analog: Dust-Storm Detection with Quantum-Disciplined Aerial Beacons

Abstract

Dust activity is a dominant driver of Martian weather and a persistent operational risk for surface missions. Reliable early detection at low altitude remains scarce because most observations are from orbit or from sparse surface masts. This paper develops a Mars-analog framework that uses autonomous aerial “beacons” carrying multi-modal dust sensors, local machine learning for classification, and chip-scale quantum-disciplined clocks for precise time stamping. A lightweight blockchain ledger provides tamper-evident provenance and event ordering for delay-tolerant communications. Conceptual evaluations in terrestrial analogs indicate improved detection accuracy, reduced false alarms, and shorter alert latency relative to static mast baselines, while meeting rotorcraft power and mass constraints. The security and autonomy stack draws on AI-plus-blockchain approaches for aerial swarms and surveillance, and the timing concept is grounded in compact quantum technologies that enable resilient synchronization in navigation-denied settings. The architecture complements orbiter weather mapping and rover meteorology and can transfer to Earth environments where satellite navigation and networks are degraded