Adaptive Vessel Tracking Algorithm for Collision Prediction
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Solution Overview
Problem
Existing vessel monitoring systems fail to accurately track and predict collision dangers for diverse vessel types, particularly high-speed and connected vessels like tug-barges, due to reliance on single tracking algorithms and limited sensor fusion, leading to inadequate real-time analysis and management.
Innovation Solution
A vessel monitoring system that utilizes a multi-sensor fusion processor to combine AIS and radar data, applying adaptive tracking algorithms based on detailed vessel characteristics, such as steering parameters, to calculate and display collision dangers, and provides real-time navigation guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single tracking algorithm is used for all vessels, then the system complexity is reduced, but the tracking precision and collision danger prediction accuracy deteriorate for diverse vessel types
Solution Approach 1:
The patent segments the tracking algorithm into multiple types (e.g., constant velocity model, constant acceleration model, maneuvering model) and selects different algorithms based on vessel type characteristics. This allows each vessel category to receive specialized tracking treatment, improving precision without requiring a single overly complex universal algorithm.
Solution Approach 2:
The system dynamically selects and switches between different tracking algorithms based on real-time vessel type identification and motion characteristics. This dynamic adaptation enables the system to optimize tracking precision for each vessel while maintaining manageable overall system complexity through modular algorithm design.
2Measurement precision
If multi-sensor fusion is implemented, then the measurement precision and collision prediction accuracy are improved, but the device complexity increases
Solution Approach 1:
The patent merges data from multiple sensors (AIS, radar, camera) into a unified tracking and prediction system. By combining the complementary strengths of each sensor type, the system achieves superior collision prediction accuracy while managing complexity through integrated data processing architecture.
Solution Approach 2:
The multi-sensor fusion system is designed with universal processing capabilities that handle different sensor types through standardized interfaces and algorithms. This multi-functional approach allows the same system architecture to process various sensor inputs, reducing overall system complexity despite the diversity of sensors involved.
3Measurement precision
If adaptive tracking algorithms based on vessel characteristics are applied, then the tracking precision for diverse vessel types is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary classification of vessels by type and pre-assigns appropriate tracking algorithms based on vessel characteristics. This preliminary action avoids the need for complex real-time analysis of each vessel's individual features, reducing processing time while maintaining high tracking precision through algorithm-vessel matching.
Solution Approach 2:
The patent changes key tracking parameters (such as update frequency, prediction horizon, and algorithm selection) based on vessel type and situation. By adjusting parameters rather than running full complex algorithms for all vessels, the system maintains high precision for critical cases while reducing overall computational burden and processing time.
Data Source
AI summary
Provided is a vessel monitoring method of a vessel monitoring system, which includes receiving first vessel information from an automatic identification system message output from a vessel, receiving second vessel information on the vessel from a port management information system, selecting a vessel tracking parameter on a basis of the first vessel information and the second vessel information, and tracking the vessel by using a tacking algorithm corresponding to the vessel tracking parameter.