Search model based on Kalman Filter and Monte Carlo simulation

Area of Science:

• Marine robotics and autonomous systems
• Search and rescue technologies
• Oceanographic modeling

Background:

• Locating distressed submersibles is challenging due to dynamic marine environments.
• Accurate prediction of submersible location is crucial for effective search and rescue (SAR) operations.
• Existing methods may lack the precision needed for complex underwater scenarios.

Purpose of the Study:

• To develop an advanced method for predicting the location of faulty or missing submersibles.
• To provide robust information support for SAR missions using predictive modeling.
• To improve the probability of successfully locating submersibles in distress.

Main Methods:

• Utilized the HYCOM model for simulating marine environments.
• Applied the Kalman filter model for real-time submersible position prediction.
• Employed Monte Carlo simulations to analyze probability distributions of submersible location post-fault.
• Introduced a nearest neighbor correlation algorithm for multi-target tracking.

Main Results:

• Quantitatively determined the minimum plane projection area for initial search deployment.
• Analyzed the probability of submersible detection over time and with cumulative search efforts.
• Demonstrated the advancement of the proposed method through comparative analysis.
• Successfully predicted the motion of multiple submersibles within the same area.

Conclusions:

• The proposed Kalman filter and Monte Carlo simulation approach significantly enhances submersible search and rescue efficiency.
• The method provides a reliable framework for predicting submersible locations and optimizing search strategies.
• Integration of nearest neighbor correlation algorithm improves multi-submersible tracking capabilities.