SINS/DVL/USBL Navigation Algorithm Based on Tight Integration

Abstract

For autonomous undersea vehicle’s requirement of high-precision and high-reliability navigation and positioning in complex underwater environments, a tightly integrated navigation algorithm involving strapdown inertial navigation system(SINS)/Doppler velocity log(DVL)/ultra-short baseline(USBL) positioning system was proposed. A SINS/DVL tightly integrated measurement equation based on frequency shift measurement and a SINS/USBL tightly integrated measurement equation based on relative measurement information were established. SINS, DVL, and USBL information were fused using a concentrated Kalman filter. In view of the decline of navigation accuracy caused by the complex underwater environment, the DVL and USBL data outliers were fully considered, and the fault data were detected and isolated by Chi-square detection. In addition, the measurement equation dimensions were updated in real time to ensure the precision of the system. The simulation results show that the proposed algorithm has higher positioning precision than other integrated model algorithms. Compared with the traditional SINS/DVL/USBL concentrated filtering method based on velocity measurement and relative position measurement, the precision can be improved by about 23%. In the case of DVL data failure, the positioning error only increases by 5.2% compared with the normal condition. In the case of USBL data failure, the positioning error increases by 165.4% compared with the normal condition, and the robustness and stability are significantly better than the SINS/DVL/USBL concentrated filtering navigation algorithm based on other measurements. Therefore, it can achieve high-precision and high-reliability underwater navigation and positioning.

Keywords

• autonomous undersea vehicle
• tight integration
• strapdown inertial navigation system
• doppler velocity log
• ultra-short baseline
• information fusion