Optimal Route Generation and Performance Evaluation for Manned Aircraft-Based Ship Detection in Maritime Search and Rescue

Maritime Search and Rescue (SAR) operations are inherently challenging due to the vast and unpredictable nature of the ocean environment, which presents complexities such as large search areas, dynamic environmental conditions, andlimited visibility. The critical nature of these operations necessitates rapid and efficient response capabilities, yet traditional search methods often fall short in addressing these challenges effectively. To address these limitations, this study introduces a novel framework for optimizing flight paths of manned aircraft, aiming to significantly enhance detection performance during SAR missions. The proposed framework specifically targets the detection of small vessels under 10 tons, which are statistically more prone to accidents. This study employs advanced predictive modeling to estimate the locations of drifting vessels, leveraging environmental data and vessel dynamics. The detection conditions were rigorously calculated based on the specifications of onboard sensors, including their resolution, range, and the environmental conditions affecting visibility. From these detection parameters, the minimum detectable area and effective detection range were derived. This enabled the formulation of both optimized and non-optimized flight paths tailored to the predicted distribution of target vessels. A comprehensive simulation-based evaluation was conducted to compare the effectiveness of the proposed flight paths. The results demonstrated that optimized flight paths consistently provided superior detection performance by minimizing search time and improving the probability of vessel detection. In particular, the study found that regional characteristics, such as coastal geography and vessel distribution patterns, had a significant impact on the effectiveness of different search strategies. Optimized paths, which took into account these regional factors, showed a marked improvement in detection efficiency compared to non-optimized paths, which were less adaptable to varying conditions. The findings of this study underscore the importance of customized path planning in maritime SAR missions, highlighting that a one-size-fits-all approach is inadequate for the diverse scenarios encountered in real-world operations. By utilizing predictive analytics and sensor-based detection modeling, the proposed framework offers a significant advancement in SAR mission planning, providing actionable insights into optimizing flight paths for enhanced detection performance. The implications of this work extend beyond SAR operations, with potential applications in maritime surveillance, environmental monitoring, and emergency response.