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Summary

This study introduces a new integrated navigation algorithm using a Cubature Kalman Filter (CKF) to improve marine navigation accuracy. The algorithm effectively reduces positional errors caused by sensor asynchrony in Strapdown Inertial Navigation Systems (SINS), Beidou (BD), and Doppler Velocity Logs (DVL).

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Area of Science:

  • Navigation Systems Engineering
  • Sensor Fusion
  • Kalman Filtering

Background:

  • Integrated navigation systems combine Strapdown Inertial Navigation Systems (SINS), Beidou (BD), and Doppler Velocity Logs (DVL) for enhanced marine accuracy.
  • Multisensor asynchrony introduces errors in integrated navigation, traditionally addressed by increasing computational complexity.

Purpose of the Study:

  • To propose an innovative integrated navigation algorithm addressing multisensor asynchrony.
  • To improve the accuracy and efficiency of SINS/BD/DVL integrated navigation systems.

Main Methods:

  • Developed a nonlinear system and observation model for the SINS/BD/DVL integrated system.
  • Introduced a new sampling principle to optimize multisensor information utilization.
  • Applied a Cubature Kalman Filter (CKF) for improved filtering accuracy.

Main Results:

  • Numerical simulations demonstrated effective reduction in positional error.
  • The proposed algorithm significantly improved the accuracy of the SINS/BD/DVL integrated navigation system.
  • Outperformed traditional Extended Kalman Filter (EKF) based algorithms.

Conclusions:

  • The novel nonlinear integrated navigation algorithm is feasible and efficient for marine applications.
  • The CKF-based approach effectively mitigates errors from multisensor asynchrony.
  • Enhanced accuracy in integrated navigation systems is achieved through optimized sensor fusion.