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Multi-Objective Optimization for Aerial Surveillance Missions

Agus Budiyono, Vishnu Kumar Kaliappan

Abstract


This technical note explores the application of multi-objective optimization principles to aerial surveillance mission planning, presenting a conceptual framework for addressing the complex trade-offs inherent in such operations. Modern surveillance missions must balance multiple competing objectives including maximizing area coverage, minimizing mission time and fuel consumption, maintaining adequate sensor resolution, and reducing exposure to threats. This note discusses how multi-objective optimization approaches, particularly Pareto-based methods, can provide mission planners with a spectrum of viable solutions rather than a single optimal point, thereby accommodating diverse operational priorities and constraints. Key considerations such as aircraft performance limitations, sensor capabilities, airspace restrictions, and mission-specific requirements are examined in the context of the optimization framework. The conceptual formulation presented here identifies the principal objective functions and constraints relevant to surveillance operations, while acknowledging the practical challenges of implementation. This work serves as a foundation for future rigorous mathematical development and algorithmic implementation, highlighting the potential benefits of systematic multi-objective approaches over traditional single-objective or heuristic planning methods. The framework is broadly applicable to various surveillance platforms and mission types, offering a structured methodology for improving operational efficiency and decision-making in aerial surveillance contexts.

Keywords


multi-objective optimization, aerial surveillance, mission planning, Pareto optimality.

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References


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