A SINS/DVL Integrated Positioning System through Filtering Gain Compensation Adaptive Filtering.

Area of Science:

• Navigation Systems Engineering
• Robotics and Control Systems
• Signal Processing

Background:

• Strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) integrated systems face positioning errors due to complex environments and sensor drift.
• DVL velocity information alone is insufficient to fully correct SINS navigation errors, leading to reduced accuracy and instability.

Purpose of the Study:

• To propose an improved SINS/DVL integrated positioning system using filtering gain compensation adaptive filtering technology.
• To enhance positioning accuracy and stability for autonomous underwater vehicles (AUVs).

Main Methods:

• An indirect filtering approach models navigation parameters using integrated positioning error.
• A filtering gain compensation adaptive filter, combined with a strong tracking filter, fuses position information.
• The filtering gain compensation algorithm utilizes error statistics of positioning parameters.

Main Results:

• The developed method effectively filters positioning parameters and identifies navigation parameter errors.
• Simulation results demonstrate significant enhancement in positioning accuracy for the SINS/DVL system.
• The adaptive filtering technology improves the overall performance of integrated navigation systems.

Conclusions:

• The proposed filtering gain compensation adaptive filtering technology offers a robust solution for SINS/DVL integrated positioning.
• This approach effectively mitigates navigation errors, leading to high-precision and stable positioning for AUVs.
• The study validates the efficacy of adaptive filtering in improving the accuracy of autonomous vehicle navigation.