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Study on the Robust Filter Method of SINS/DVL Integrated Navigation Systems in a Complex Underwater Environment.

Tianlong Zhu¹, Jian Li¹, Kun Duan¹

¹College of Information Science and Engineering, Hohai University, Changzhou 213001, China.

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|October 26, 2024

Summary

This study introduces an improved adaptive filtering algorithm for strapdown inertial navigation system (SINS)/Doppler Velocity Log (DVL) integration. The enhanced method boosts navigation accuracy and stability by refining measurement noise estimation, outperforming existing algorithms.

Keywords:

Doppler Velocity Log error correction integrated positioning and navigation system strapdown inertial navigation system

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Area of Science:

Navigation Systems Engineering
Signal Processing
Control Theory

Background:

Strapdown Inertial Navigation System (SINS)/Doppler Velocity Log (DVL) integrated systems face navigation accuracy degradation due to measurement noise characteristics.
Traditional adaptive filtering algorithms often encounter non-positive definite matrix issues, hindering accurate noise estimation.
Accurate estimation of measurement noise is critical for robust performance in integrated navigation.

Purpose of the Study:

To develop an improved adaptive filtering algorithm for SINS/DVL systems to enhance navigation accuracy and stability.
To address the challenge of measurement noise impacting integrated navigation performance.
To overcome limitations of existing adaptive filtering methods, particularly the non-positive definite matrix problem.

Main Methods:

An improved adaptive filtering algorithm based on the Sage-Husa adaptive Kalman filtering algorithm is proposed.
The method incorporates upper and lower thresholds, determined by a discrimination factor, to manage abnormal measurement data.
Innovation covariance is adjusted, and measurement noise is re-estimated using a decision factor based on the innovation.

Main Results:

The proposed algorithm demonstrates superior navigation accuracy and stability compared to the classical Kalman filter (KF).
Filtering performance significantly surpasses that of the Sage-Husa algorithm.
Simulation results show a 49.44% reduction in relative position error compared to the Sage-Husa filtering method.

Conclusions:

The improved adaptive filtering algorithm effectively enhances navigation accuracy and stability in SINS/DVL systems.
The proposed method provides a robust solution for dealing with measurement noise and abnormal data.
This algorithm offers a significant advancement over existing adaptive filtering techniques for integrated navigation.