Multi-Target Data Association Algorithm in Underwater BOT System with Spatial Bias and Signal Delay

Area of Science:

• Robotics and Autonomous Systems
• Signal Processing
• Underwater Acoustics

Background:

• Unmanned underwater vehicles (UUVs) face challenges in multi-target tracking due to perceptual inconsistencies.
• Accurate target state estimation in bearing-only tracking (BOT) requires addressing sensor bias and data association.

Purpose of the Study:

• To develop a robust multi-target data association (MDA) framework for underwater BOT systems.
• To explicitly model and compensate for sensor spatial bias and signal propagation delay.

Main Methods:

• Introduced a measurement model incorporating spatial bias and signal delay.
• Developed an initial target state estimation using Maximum Likelihood Estimation (MLE) and Particle Swarm Optimization (PSO).
• Formulated a cost function for data association and employed an iterative MDA algorithm with the Expectation-Maximization (EM) method.

Main Results:

• The proposed MDA framework effectively mitigates signal delay and spatial bias effects.
• The EM-based spatial bias estimation method demonstrated accurate bias estimation capabilities.
• Monte Carlo simulations validated the overall effectiveness of the MDA framework.

Conclusions:

• The developed MDA framework significantly improves multi-target perceptual consistency in UUV-based BOT systems.
• Accurate compensation for sensor biases and precise data association are crucial for reliable underwater target tracking.