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PMHT Approach for Multi-Target Multi-Sensor Sonar Tracking in Clutter.

Xiaohua Li¹, Yaan Li², Jing Yu³

¹School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China. lxhxy2009@163.com.

Sensors (Basel, Switzerland)

|November 13, 2015

Summary

This study introduces a new probabilistic multi-hypothesis tracker (PMHT) for multi-sensor sonar tracking in cluttered underwater environments. The novel approach improves target tracking accuracy and handles data association challenges effectively.

Keywords:

data association extended Kalman filter (EKF)multi-target multi-sensor sonar tracking probabilistic multi-hypothesis tracker (PMHT)unscented Kalman filter (UKF)

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Area of Science:

Underwater acoustics
Signal processing
Robotics and autonomous systems

Background:

Multi-sensor sonar tracking offers reduced uncertainty and stealth capabilities.
Underwater environments present challenges like low detection probability and high false alarms due to reverberation.
Existing methods struggle with data association in cluttered conditions.

Purpose of the Study:

To propose a novel probabilistic multi-hypothesis tracker (PMHT) for multi-target multi-sensor sonar tracking.
To address the complexities of underwater environments, including clutter and data association ambiguity.
To enhance target tracking performance and computational efficiency.

Main Methods:

Development of a probabilistic multi-hypothesis tracker (PMHT).
Integration of Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) for nonlinear measurement models.
Handling of unknown measurements-to-targets and measurements-to-transmitters data association.

Main Results:

The proposed PMHT algorithm significantly improves target tracking performance in cluttered environments.
The algorithm effectively manages data association ambiguities.
Simulation results demonstrate a low computational load.

Conclusions:

The novel PMHT approach provides a robust solution for multi-target multi-sensor sonar tracking in challenging underwater conditions.
The integration of EKF and UKF enhances the tracker's ability to handle nonlinearities.
The algorithm offers improved accuracy and efficiency compared to existing methods.