This analysis demonstrates improved performance in operations by optimizing resource management with Monte Carlo simulations, suggesting enhanced flexibility in system design.
Multi-Function Radar(MFR) systems must concurrently perform search and tracking within strict time and energy limits, rendering resource allocation critical. This study applies Particle Swarm Optimization(PSO)-noted for its simplicity and global search capabilities-to optimize resource management in MFR operations. Three operational presets(SEARCH, BALANCED, and TRACK) were predefined to represent distinct operational modes. Monte Carlo simulations quantified each preset’s performance in search coverage, tracking retention, and time-budget compliance. Results demonstrated clear differentiation between presets, indicating that operators can seamlessly shift from search-oriented to tracking-oriented operations by merely adjusting preset parameters without redesigning system architecture. These findings provide a practical framework for enhancing flexibility in both design and operational phases. Future work will incorporate multi-objective optimization and adaptive parameter scheduling to further improve responsiveness to dynamic battlefield environments.
No takes yet. Share an insight, caveat, or question.
Park et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: