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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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A TLS-Motivated Non-Iterative Robust Square-Root Cubature Kalman Filter for Bearings-Only Tracking.

Chaoqi Li1, Hao Wu1, Guoxu Zeng1

  • 1Information and Navigation College, Air Force Engineering University, Xi'an 710077, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary

A new robust square-root cubature Kalman filter (RSCKF) effectively handles measurement outliers in bearings-only tracking. This filter maintains accuracy while significantly improving robustness against corrupted data, outperforming standard methods.

Keywords:
bearings-only trackingrobust nonlinear filteringsensor outlierssquare-root cubature Kalman filtertotal least squares

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Magnetic Tweezers for the Measurement of Twist and Torque
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Published on: May 19, 2014

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Last Updated: Jun 13, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Magnetic Tweezers for the Measurement of Twist and Torque
11:41

Magnetic Tweezers for the Measurement of Twist and Torque

Published on: May 19, 2014

Area of Science:

  • Signal Processing
  • Estimation Theory
  • Target Tracking

Background:

  • Nonlinear bearings-only target tracking is susceptible to performance degradation caused by measurement outliers.
  • Corrupted observations can lead to large innovations and filter divergence, compromising tracking accuracy.

Purpose of the Study:

  • To propose a non-iterative robust square-root cubature Kalman filter (RSCKF) for bearings-only tracking in the presence of measurement outliers.
  • To introduce a robust mechanism within the square-root cubature Kalman filtering framework to mitigate the impact of outliers.

Main Methods:

  • Developed a closed-form equivalent weighting and rejection mechanism for robustness, inspired by TLS-type errors-in-variables interpretation.
  • Preserved the derivative-free square-root filtering structure, avoiding iterative computations.
  • Determined weighting thresholds from normalized innovation statistics for outlier down-weighting and rejection.

Main Results:

  • RSCKF achieved accuracy comparable to standard SCKF under nominal conditions.
  • Demonstrated significantly improved robustness against measurement outliers, with limited Mean Squared Error (MSE) increase even under severe contamination.
  • Outperformed seven other filters in severe outlier scenarios, maintaining tracking consistency and showing minimal computational overhead.

Conclusions:

  • The proposed RSCKF offers a robust and computationally efficient solution for bearings-only tracking with measurement outliers.
  • It effectively mitigates the detrimental effects of corrupted data, ensuring reliable target tracking performance.
  • RSCKF represents a valuable advancement for applications requiring high accuracy in noisy environments.