Tracking Multiple Targets Using Bearing-Only Measurements in Underwater Noisy Environments

Area of Science:

• Robotics and Control Systems
• Signal Processing
• Underwater Acoustics

Background:

• Tracking multiple targets in noisy underwater environments presents significant challenges.
• Bearing-only measurements are passive but suffer from large range uncertainty.
• Existing methods like single Extended Kalman Filter (EKF) within Gaussian Mixture Probability Hypothesis Density (GM-PHD) filters show limitations.

Purpose of the Study:

• To develop an improved multi-target tracking algorithm for underwater scenarios using bearing-only measurements.
• To address the range uncertainty inherent in passive sonar data.
• To enhance the performance of the GM-PHD filter in complex acoustic environments.

Main Methods:

• Utilizing the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter framework.
• Integrating Extended Kalman Filters (EKF) to handle nonlinear bearing-only measurements.
• Generating multiple target samples from each bearing measurement, distributed across feasible ranges.
• Updating each target sample individually with EKF measurement updates, running multiple EKFs concurrently.

Main Results:

• The proposed method effectively mitigates the poor performance associated with single EKF application in GM-PHD filters.
• Demonstrated superior multi-target tracking accuracy in simulated noisy underwater environments.
• Successfully managed range uncertainty by distributing target samples and employing multiple EKFs.

Conclusions:

• The novel approach of using multiple EKFs with distributed target samples within a GM-PHD filter significantly improves multi-target tracking performance.
• This method offers a robust solution for passive sonar systems operating in challenging underwater conditions.
• The findings pave the way for more effective underwater surveillance and navigation systems.