Efficiently detecting the nearest navigational dangers in Electronic Chart Display and Information Systems (ECDIS) remains pivotal for maritime safety. However, the software implementation of ADMAR(Automatic Distance measurement and Ranging) functionality faced challenges, necessitating extensive computations across ENC cells and impacting real-time performance. To address this, we present a novel method employing dynamic programming. Our proposed algorithm strategically organizes nodes into a tree structure, optimizing the search process towards nodes likely to contain navigational hazards. Implementation of this method resulted in a notable sevenfold reduction in computation time compared to the conventional Brute Force approach. Our study established a direct correlation between the ADMAR functionality and node count, achieving error margins deemed acceptable for practical navigation scenarios. Despite this theoretical progress, minor errors in results prompt further refinement. Consequently, future iterations will explore varying values for N, considering hierarchy and cell sizes to enhance algorithmic precision. This research signifies a potential advancement in optimizing navigational danger detection within ECDIS, offering a promising avenue for improved efficiency. By introducing a dynamic programming-based approach, we have streamlined the detection process while acknowledging the scope for algorithmic refinement in subsequent studies. Our findings underline the feasibility of employing dynamic programming to enhance navigational danger detection, emphasizing its potential in ensuring maritime safety. This work lays a foundation for future research endeavors, aiming to fine-tune algorithms and advance navigational safety measures in ECDIS.

1. Introduction
2. The Analysis of Navigational Danger Detection in ENC
    2.1. Data Structure of S-57 ENC
    2.2. ADMAR
3. Optimal Algorithm for Nearest Navigational Danger Detection
    3.1. Hierarchical Aggregation Procedure
    3.2. Search Procedure
4. Application and Validation of Algorithm
    4.1. Validation Environment
    4.2. Validation Results
5. Conclusions
6. Acknowledgement
References

• Jisoo KIM(R&D Center, mapsea Corp., Seoul, Korea)
• Hongrai CHO(R&D Center, mapsea Corp., Seoul, Korea)
• Unggyu KIM(R&D Center, mapsea Corp., Seoul, Korea)
• Byunggong HWANG(R&D Center, mapsea Corp., Seoul, Korea)