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Adaptive Federated IMM Filter for AUV Integrated Navigation Systems.

Weiwei Lyu^1,2, Xianghong Cheng^1,2, Jinling Wang³

¹School of Instrument Science & Engineering, Southeast University, Nanjing 210096, China.

Sensors (Basel, Switzerland)

|December 2, 2020

Summary

This study introduces an adaptive federated interacting multiple model (IMM) filter for autonomous underwater vehicles (AUVs) navigating complex underwater environments. The proposed filter enhances navigation accuracy and reliability, outperforming existing methods.

Keywords:

AUV federated Kalman filter information sharing coefficient integrated navigation interacting multiple model (IMM)

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Area of Science:

Robotics
Navigation Systems
Signal Processing

Background:

Accurate navigation is crucial for autonomous underwater vehicle (AUV) operations in challenging underwater environments.
Existing navigation systems face limitations in complex conditions, impacting reliability and precision.

Purpose of the Study:

To develop an advanced navigation filter for AUVs operating in complex underwater settings.
To enhance the accuracy and reliability of AUV integrated navigation systems.

Main Methods:

Proposes an adaptive federated interacting multiple model (IMM) filter, integrating adaptive federated filtering with IMM algorithms.
Dynamically adjusts information sharing coefficients based on local system performance.
Implements real-time model switching for local systems to adapt to changing external disturbances.
Constructs an AUV integrated navigation system model incorporating SINS/DVL and SINS/TAN measurements.

Main Results:

The adaptive federated IMM filter significantly improves the accuracy and reliability of AUV integrated navigation.
Experimental results demonstrate superior performance compared to traditional federated Kalman filter and adaptive federated Kalman filter.
The filter effectively handles complex underwater environments and external disturbances.

Conclusions:

The proposed adaptive federated IMM filter offers a robust solution for AUV navigation in challenging underwater conditions.
This approach enhances the operational capabilities and mission success rates of AUVs.
The adaptive nature and real-time model switching provide a significant advantage over previous methods.