# GNSS/SINS/DVL integrated navigation algorithm based on adaptive differential Kalman filtering

## TL;DR

This paper introduces a new navigation algorithm that improves accuracy and stability for underwater vehicles in complex marine environments.

## Contribution

The novel contribution is an adaptive differential Kalman filtering method for integrated navigation data processing.

## Key findings

- The proposed ADKF method significantly improves parameter estimation accuracy compared to standard Kalman filters.
- The algorithm enhances stability in complex marine navigation scenarios.
- It is well-suited for post-processing navigation data in dynamic underwater environments.

## Abstract

The global navigation satellite system/strapdown inertial navigation system/doppler velocity logger (GNSS/SINS/DVL) integrated navigation system leverages the complementary advantages of its three subsystems to provide essential navigation information—such as attitude, velocity, and position—for carriers operating in marine environments. However, unmanned underwater vehicle (UUV) faces challenges like observation anomalies and dynamic model inaccuracies during dynamic maritime navigation and positioning. These issues make it difficult for the standard Kalman filter (KF) to cope with the complexities of the ocean environment, thereby reducing the accuracy of navigation parameter estimates. To address this, this study introduces an adaptive differential Kalman filtering (ADKF) method for processing integrated navigation data. Experimental results indicate that, compared with the KF, the proposed algorithm significantly enhances the accuracy and stability of parameter estimation, making it well-suited for post-processing integrated navigation data in complex marine settings.