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This study introduces novel adaptive estimators for tracking maneuvering targets, improving accuracy by combining variational inference and change-point detection for robust state and noise parameter estimation.

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Area of Science:

  • Signal Processing
  • Control Systems
  • Bayesian Inference

Background:

  • Tracking sharply maneuvering targets presents challenges due to abrupt parameter changes in state-space models.
  • Existing adaptive Kalman filters struggle with initial settings and non-stationarity.

Purpose of the Study:

  • Develop novel variational adaptive state estimators for joint target state and process noise parameter estimation.
  • Enhance the capability of tracking sharply maneuvering targets in dynamic environments.

Main Methods:

  • Proposed two recursive estimators: change-point-based adaptive Kalman filter (CPAKF) and change-point-based adaptive Kalman smoother (CPAKS).
  • Combined variational inference with online Bayesian change-point detection for parameter and state estimation.
  • Calculated run-length probability and approximated joint posterior using variational inference.

Main Results:

  • The proposed CPAKF and CPAKS methods demonstrate robustness to initial iterative value settings.
  • Achieved improved tracking performance for sharply maneuvering targets compared to existing methods.
  • Change-point detection facilitated adaptive sliding window length in CPAKS for non-stationary sequences.

Conclusions:

  • The novel variational adaptive estimators effectively handle abrupt parameter changes in maneuvering target tracking.
  • CPAKF and CPAKS offer a robust and adaptive solution for complex tracking scenarios.
  • Validated performance using both synthetic and real-world maneuvering target datasets.