Multi-Sonar Distributed Fusion for Target Detection and Tracking in Marine Environment.

Area of Science:

• Marine acoustics
• Signal processing
• Data fusion

Background:

• Distributed multi-sonar systems are crucial for marine target detection and tracking.
• Realistic scenarios present challenges including uncertain track origins and out-of-sequence (OOS) data due to clutter and system delays.
• Existing fusion methods struggle with these complexities, impacting performance.

Purpose of the Study:

• To propose a novel fusion method that optimally handles multi-lag out-of-sequence tracks (OOST) and track origin uncertainty.
• To develop a technique for timely detection of randomly appearing marine targets.
• To enhance the performance of distributed multi-sonar fusion systems.

Main Methods:

• Developed a multiple forward prediction-integrated equivalent measurement fusion (MFP-IEMF) method within a distributed fusion framework with memory.
• Integrated a random central track initialization technique for rapid track confirmation.
• Implemented the method for both synchronous and asynchronous multi-sonar track fusion.

Main Results:

• The MFP-IEMF method optimally fuses multi-lag OOST with track origin uncertainty.
• The proposed random central track initialization effectively detects randomly born targets.
• Numerical results demonstrate performance comparable to optimal reprocessing methods and superior to discarding or simpler fusion methods.

Conclusions:

• The MFP-IEMF method offers an optimal solution for distributed multi-sonar fusion under challenging conditions.
• The proposed techniques significantly improve marine target detection and tracking accuracy.
• This work advances the capabilities of sonar data fusion systems.