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Maneuvering Target Tracking Using Simultaneous Optimization and Feedback Learning Algorithm Based on Elman Neural

Huajun Liu1,2, Liwei Xia3, Cailing Wang4,5

  • 1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210014, China. liuhj@njust.edu.cn.

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|April 17, 2019
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Summary

This study introduces a novel Elman neural network-based unscented Kalman filter (ENN-UKF) for tracking maneuvering targets. The ELM-UKF algorithm improves filtering precision by adaptively tuning models and refining state estimations, outperforming existing methods.

Keywords:
Elman neural networkmaneuvering target trackingsimultaneous optimization and feedback learning

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Area of Science:

  • Signal Processing
  • Artificial Intelligence
  • Control Systems

Background:

  • Tracking maneuvering targets presents significant challenges due to unpredictable motion.
  • Classical statistical methods often struggle with the complexities of target maneuvers.

Purpose of the Study:

  • To propose a simultaneous optimization and feedback learning algorithm for maneuvering target tracking.
  • To enhance state estimation accuracy and adaptive model tuning in dynamic environments.

Main Methods:

  • Development of an Elman neural network (ENN)-based unscented Kalman filter (UKF), termed ELM-UKF.
  • Integration of a feedback strategy to adaptively tune the dynamic model's error covariance matrix.
  • Incorporation of an optimization strategy to refine the state vector for improved estimation.

Main Results:

  • The ELM-UKF algorithm demonstrated superior filtering precision in Monte Carlo experiments.
  • Online training using filter residual, innovation, and gain matrix enabled simultaneous maneuver feedback and optimized estimation.
  • The proposed method showed better performance compared to most existing maneuvering target tracking algorithms.

Conclusions:

  • The ELM-UKF offers an effective solution for maneuvering target tracking.
  • The simultaneous optimization and feedback learning approach significantly enhances tracking accuracy.
  • This algorithm provides a robust and adaptive method for complex dynamic systems.