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Extended target tracking with mobility based on GPR-AUKF.

Renli Zhang1, Yan Zhang2, Jintao Chen1

  • 1School of Aeronautics and Astronautics, Sun Yat-sen University, 518107, Shenzhen, China.

Heliyon
|December 24, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new Gaussian process regression-adaptive unscented Kalman filter (GPR-AUKF) for precisely tracking extended targets. The GPR-AUKF method effectively handles nonlinearities and time-varying noise, outperforming traditional filters.

Keywords:
Adaptive unscented Kalman filterExpectation maximization algorithmExtended targetGaussian process regressionTime-varying noise

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Area of Science:

  • Robotics and Control Systems
  • Signal Processing
  • Machine Learning

Background:

  • Estimating kinematic states and extents of extended targets is complex, especially for mobile targets.
  • Traditional Extended Kalman Filters (EKF) may struggle with high nonlinearity and time-varying noise.
  • Accurate tracking requires adapting to changing measurement noise covariance.

Purpose of the Study:

  • To develop an advanced filtering technique for high-precision tracking of extended targets.
  • To improve upon existing Kalman filter methods by incorporating Gaussian Process Regression and adaptive capabilities.
  • To address the limitations of constant measurement noise covariance assumptions in dynamic environments.

Main Methods:

  • Embedding the Unscented Kalman Filter (UKF) within Gaussian Process Regression (GPR) to handle nonlinearities.
  • Developing an Adaptive Unscented Kalman Filter (AUKF) integrated with GPR (GPR-AUKF).
  • Utilizing the Expectation Maximization (EM) algorithm within GPR-AUKF for real-time updates of measurement noise covariance and target state estimation.

Main Results:

  • The proposed GPR-AUKF algorithm demonstrates superior accuracy in tracking extended targets.
  • Experimental results confirm the robustness of GPR-AUKF compared to conventional methods.
  • The adaptive nature of GPR-AUKF effectively manages time-varying measurement noise covariance.

Conclusions:

  • The GPR-AUKF algorithm offers a significant advancement for extended target tracking in challenging environments.
  • This method provides a robust solution for scenarios with nonlinear dynamics and uncertain noise characteristics.
  • The real-time adaptation of measurement noise covariance is key to the enhanced performance.