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Related Experiment Videos

Tracking a maneuvering target using neural fuzzy network.

Fun-Bin Duh1, Chin-Teng Lin

  • 1Department of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu, Taiwan, ROC. william@falcon3.en.nctu.edu.tw

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 17, 2004
PubMed
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This study introduces a Kalman filter with a self-constructing neural fuzzy inference network (KF-SONFIN) for accurate target tracking. The KF-SONFIN effectively detects target maneuvers, improving tracking accuracy over traditional methods.

Area of Science:

  • Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Automatic target tracking systems face challenges in detecting target maneuvers to prevent miss-tracking.
  • Traditional algorithms like variable dimension filter (VDF) and input estimation (IE) are computationally intensive and difficult for real-time implementation.
  • Standard neural networks often suffer from slow convergence and large network sizes, with heuristic structure determination.

Purpose of the Study:

  • To propose a novel, fast, and highly accurate target maneuver detection and tracking technique.
  • To overcome the limitations of traditional algorithms and standard neural networks in automatic target tracking.
  • To develop a Kalman filter integrated with a self-constructing neural fuzzy inference network (KF-SONFIN) for enhanced performance.

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Main Methods:

  • A developed standard Kalman filter is combined with a self-constructing neural fuzzy inference network (SONFIN).
  • The SONFIN is trained using simulated target trajectories, including maneuver information.
  • The algorithm automatically determines an economic network size and learns quickly without altering the Kalman filter structure or explicitly modeling maneuvering targets.

Main Results:

  • The trained SONFIN can accurately detect maneuver occurrence, magnitude, and cessation.
  • The KF-SONFIN algorithm demonstrates superior estimation accuracy compared to traditional IE and VDF methods.
  • The proposed method achieves fast learning processes and an efficient network size.

Conclusions:

  • The KF-SONFIN algorithm offers a significant advancement in automatic target tracking by effectively detecting maneuvers.
  • It provides a more accurate and computationally efficient solution compared to existing methods.
  • This technique enhances the robustness and reliability of target tracking systems.