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Maximum correntropy delay Kalman filter for SINS/USBL integrated navigation.

Bo Xu1, Xiaoyu Wang1, Jiao Zhang1

  • 1Department of Automation, Harbin Engineering University, Harbin 150001, China; Engineering Research Center of Navigation Instruments, Harbin Engineering University, Harbin 150001, China.

ISA Transactions
|April 14, 2021
PubMed
Summary

This study introduces a new algorithm for strapdown inertial navigation system/ultra-short baseline (SINS/USBL) integrated navigation, effectively handling communication delays and non-Gaussian noise in underwater environments. The proposed filter enhances accuracy and robustness for improved underwater positioning.

Keywords:
Communication delayNon-Gaussian noiseStrapdown inertial navigation systemUltra-short baseline

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Area of Science:

  • Marine technology
  • Navigation systems
  • Signal processing

Background:

  • Underwater navigation and positioning face challenges from communication delays and non-Gaussian noise.
  • Strapdown inertial navigation system/ultra-short baseline (SINS/USBL) integrated navigation systems are crucial for underwater operations.
  • Existing methods struggle with time-varying delays and non-Gaussian noise, impacting accuracy.

Purpose of the Study:

  • To propose a novel filtering algorithm for SINS/USBL integrated navigation.
  • To address challenges of time-varying communication delay and non-Gaussian noise in underwater acoustic communication.
  • To improve the accuracy and robustness of underwater navigation systems.

Main Methods:

  • Derived the measurement equation for SINS/USBL considering platform error angle.
  • Developed a distance-related time delay system model based on state inversion for USBL acoustic communication.
  • Constructed a linear recursive model using the maximum correntropy criterion to update estimation and covariance.
  • Incorporated an adaptive adjustment function to reduce computational complexity.

Main Results:

  • The proposed algorithm effectively handles communication delays and non-Gaussian noise.
  • The filter demonstrates improved accuracy and robustness compared to existing methods.
  • Simulation and experimental results validate the algorithm's performance.
  • Reduced computational complexity achieved through adaptive adjustment.

Conclusions:

  • The developed filtering algorithm significantly enhances SINS/USBL integrated navigation performance.
  • The approach provides a robust solution for underwater navigation under challenging noise and delay conditions.
  • This work contributes to more reliable and efficient underwater positioning systems.