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Adaptive Robust Unscented Kalman Filter for AUV Acoustic Navigation.

Junting Wang1, Tianhe Xu1, Zhenjie Wang2

  • 1Institute of Space Science, Shandong University, Weihai 264209, China.

Sensors (Basel, Switzerland)
|December 22, 2019
PubMed
Summary

This study introduces an adaptive robust unscented Kalman filter (UKF) to improve autonomous underwater vehicle (AUV) acoustic navigation. The new method enhances accuracy and stability by managing system noise and reducing errors from acoustic observations.

Keywords:
AUV acoustic navigationSage-Husa filteradaptive filterrobust estimationunscented Kalman filter

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Area of Science:

  • Robotics
  • Marine Engineering
  • Signal Processing

Background:

  • Autonomous underwater vehicle (AUV) acoustic navigation faces challenges from unknown system noise and observation errors in complex marine environments.
  • Classical unscented Kalman filter (UKF) algorithms struggle with dynamic model biases and gross errors, limiting navigation accuracy.
  • Robustness and adaptability are crucial for reliable AUV positioning in real-world conditions.

Purpose of the Study:

  • To develop an adaptive robust UKF for AUV acoustic navigation that addresses limitations of classical UKF.
  • To improve system noise estimation and mitigate the impact of gross errors in acoustic observations.
  • To enhance the accuracy and stability of AUV navigation systems.

Main Methods:

  • Proposed an adaptive robust UKF integrating the Sage-Husa filter for online system noise compensation.
  • Implemented a robust estimation technique using Huber's equivalent weight function to control gross error influence.
  • Validated the algorithm through simulated long baseline positioning and real marine experimental data.

Main Results:

  • The adaptive UKF effectively estimates time-varying system noise, preventing negative definite noise variance matrices.
  • The proposed adaptive robust UKF significantly reduces the impact of gross errors while adjusting system noise.
  • Experimental results demonstrate improved accuracy and stability in AUV acoustic navigation.

Conclusions:

  • The adaptive robust UKF offers a superior solution for AUV acoustic navigation compared to traditional methods.
  • The algorithm's ability to handle system noise and gross errors enhances reliability in challenging marine environments.
  • This approach contributes to more precise and stable underwater navigation for AUVs.