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Feedback Robust Cubature Kalman Filter for Target Tracking Using an Angle Sensor.

Hao Wu1, Shuxin Chen2, Binfeng Yang3

  • 1Information and Navigation College Air Force Engineering University, Fenghao east road No.1, Xi'an 710077, China. wuhaostudy@163.com.

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Summary
This summary is machine-generated.

A new robust filter improves direction of arrival (DOA) tracking by handling sensor outliers. The feedback M-estimation based robust cubature Kalman filter (FMR-CKF) enhances accuracy and robustness in tracking applications.

Keywords:
DOA trackingangle sensorcubature Kalman filterfeedback strategynonlinear systemrobust estimation

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Area of Science:

  • Signal Processing
  • Estimation Theory
  • Sensor Fusion

Background:

  • Direction of Arrival (DOA) tracking is crucial in various fields.
  • Angle sensors are susceptible to measurement outliers, degrading tracking performance.

Purpose of the Study:

  • To propose a novel nonlinear filter, the feedback M-estimation based robust cubature Kalman filter (FMR-CKF), for robust DOA tracking.
  • To address challenges posed by measurement outliers in angle sensor data.

Main Methods:

  • Developed a new equivalent weight function combining Cubature Kalman Filter (CKF) with M-estimation using Mahalanobis distance.
  • Integrated a feedback strategy with splitting and merging procedures to dynamically select between standard CKF and robust CKF.

Main Results:

  • The FMR-CKF demonstrated superior accuracy and robustness compared to standard CKF and conventional robust filters.
  • Achieved good computational efficiency, making it practical for real-time applications.
  • Reduced the probability of misjudging outliers effectively.

Conclusions:

  • The FMR-CKF offers an effective solution for robust DOA tracking in the presence of measurement outliers.
  • The proposed filter shows potential for extension to nonlinear applications with diverse sensor types.