Research on the Cooperative Target State Estimation and Tracking Optimization Method of Multi-UUV

Area of Science:

• Robotics
• Control Systems
• Ocean Engineering

Background:

• Cooperative target tracking is crucial for multi-unmanned underwater vehicle (multi-UUV) systems.
• Accurate state estimation and path optimization are key challenges in multi-UUV cooperative tracking.

Purpose of the Study:

• To develop a distributed cooperative state estimation method for multi-UUVs using bearing-only information.
• To implement an online collaborative path optimization strategy for persistent target tracking by multi-UUVs.

Main Methods:

• Established mathematical models for the multi-UUV cooperative target tracking system.
• Utilized an extended Kalman filter with interacting multiple models for distributed bearing-only target state estimation.
• Applied a federal fusion algorithm to integrate individual UUV estimations.
• Employed particle swarm optimization for online collaborative path optimization based on predicted target states.

Main Results:

• The distributed fusion filtering algorithm demonstrated superior target state estimation performance.
• The online path collaborative optimization effectively achieved persistent target tracking.

Conclusions:

• The proposed methods enhance the cooperative capabilities of multi-UUV systems in target tracking.
• Distributed estimation and predictive path optimization are vital for robust multi-UUV operations.