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Youqing Zhu1, Shilin Zhou1, Gui Gao1

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This study introduces a fast algorithm for extended target tracking using radar measurements. The method improves computational efficiency and tracking accuracy by employing hierarchical clustering and signal features for measurement partitioning.

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Area of Science:

  • Signal Processing
  • Data Association
  • Target Tracking

Background:

  • Extended targets generate multiple measurements per time step, complicating tracking.
  • Exact probability hypothesis density (PHD) filters for extended targets are computationally intractable due to partitioning requirements.

Purpose of the Study:

  • To develop a computationally efficient algorithm for extended target tracking.
  • To improve tracking performance by incorporating signal features into measurement partitioning.

Main Methods:

  • A fast partitioning algorithm based on hierarchical clustering is proposed.
  • The algorithm iteratively combines similar cells to form new partitions.
  • Pseudo-likelihoods are computed iteratively within a Gaussian-mixture PHD filter framework.
  • Signal features from emitter targets are integrated into the measurement partitioning process.

Main Results:

  • The proposed method significantly reduces computational complexity compared to exact PHD filters.
  • Improved tracking performance is demonstrated, especially in scenarios with varying clutter densities.
  • The integration of signal features enhances the accuracy of measurement partitioning.

Conclusions:

  • The developed hierarchical clustering-based partitioning algorithm offers a computationally tractable solution for extended target tracking.
  • Incorporating signal features alongside clustering provides a robust approach to enhance tracking accuracy.
  • The method presents a practical advancement for real-time applications requiring efficient extended target tracking.