An Improved Adaptive Kalman Filter for Underwater SINS/DVL System

TLDR

The main challenge of SINS/DVL navigation is external measurement noise, and the precision and stability of the Sage–Husa adaptive Kalman filter remain problematic. This paper aims to enhance the precision and stability of underwater SINS/DVL systems. The authors develop a tightly integrated SINS/DVL model that uses beam measurements directly and introduce an improved SHAKF employing variable sliding‑window estimation and a fading filter. Simulations and vehicle tests confirm the method’s effectiveness, with position accuracy surpassing that of the conventional SHAKF.

Abstract

The main challenge of Strap-down Inertial Navigation System (SINS)/Doppler velocity log (DVL) navigation system is the external measurement noise. Although the Sage–Husa adaptive Kalman filter (SHAKF) has been introduced in the integrated navigation field, the precision and stability of the SHAKF are still the tricky problems to be overcome. The primary aim of this paper is to improve the precision and stability of underwater SINS/DVL system. To attain this, a SINS/DVL tightly integrated model is established, where beam measurements are used without transforming them to 3D velocity. The proposed improved SHAKF algorithm is based on variable sliding window estimation and fading filter. The simulations and vehicle test results demonstrate the effectiveness of the proposed underwater SINS/DVL tightly integrated navigation method based on the improved SHAKF. In addition, the position accuracy of the designed method outperforms that of the SHAKF method.