Research on Registration Methods for Coupled Errors in Maneuvering Platforms.

Area of Science:

• Robotics and Autonomous Systems
• Sensor Fusion and Estimation Theory
• Navigation and Control Systems

Background:

• Single-sensor systems face performance limitations in target tracking.
• Multi-sensor fusion enhances accuracy but faces challenges with coupled errors on mobile platforms.
• Existing sensor registration methods struggle with dynamic environments and coupled errors.

Purpose of the Study:

• To develop a novel joint error estimation and registration method for multi-sensor systems on mobile platforms.
• To address the limitations of existing methods in handling coupled measurement and attitude errors in dynamic environments.
• To improve the accuracy and robustness of sensor registration in real-time applications.

Main Methods:

• Proposed a pseudo-Kalman filter (PKF) for joint error estimation and sensor registration.
• Constructed pseudo-measurements by subtracting sensor outputs and projecting into a bias space.
• Implemented a decoupling mechanism to distinguish measurement and attitude error components for real-time joint estimation.

Main Results:

• The PKF-based method demonstrated lower root mean square error (RMSE) compared to least squares (LS), maximum likelihood (ML), and standard PKF methods.
• Achieved faster convergence speed and superior estimation accuracy and robustness in shipborne simulations.
• Effectively handled coupled measurement and attitude errors in dynamic environments.

Conclusions:

• The proposed joint error estimation and registration method offers a practical and scalable solution for dynamic environments.
• This approach is particularly beneficial for maritime and aerial applications with prevalent coupled errors.
• The PKF method significantly enhances the performance of multi-sensor systems in challenging operational conditions.