In general, the strap-down inertial navigation system (SINS)/ Doppler velocity log (DVL) integrated navigation method can provide continuous and accurate navigation information for autonomous underwater vehicles (AUV). This SINS/DVL fusion is the loosely integrated method, in which DVL may contain large error or doesn’t work when some beam measurements are inaccurate or outages for complex underwater environment. To solve these problems, in this paper, a novel tightly integrated navigation method composed of a SINS, a DVL, and a pressure sensor (PS) is proposed, in which beam measurements are used without transforming them to 3-D velocity. The simulation and vehicle test show that the proposed method can significantly outperform the traditional loosely integrated method in providing estimation continuously with higher accuracy when DVL data are inaccurate or unavailable for complex environment. Compared with loosely integrated method, the position accuracy of the proposed method has improved by 32.5%.

Doppler Velocity Logs (DVL) is an important information source for underwater vehicle navigation because it can provide accurate velocity information. As the main system, SINS/DVL integrated navigation is widely used in the underwater vehicle. However, DVL scale factor error, and misalignment with inertial measurement unit (IMU) are the two sources of integration inaccuracy. A novel calibration method aided GNSS is proposed in this paper, which is designed based on velocity observation and position observation respectively. The proposed method combines the Quasi-Newton method and Quaternions method, and we name it Quasi-Newton Quaternion (QNQ) calibration method. In order to verify the performance of the proposed method, another calibration method based on the Kalman filter is introduced in simulation, vehicle tests, and Sea trial. The test results indicate that the proposed calibration method can perform a good estimation of DVL error.

Autonomous underwater vehicle (AUV) mostly relies on an integrated navigation system, which consists of a strap-down inertial navigation system (SINS) and Doppler velocity log (DVL). The integrated system provides continuous and accurate navigation information when compared to stand-alone SINS or DVL. However, the dependence of DVL signals on the acoustic environment may cause any DVL malfunction due to marine organisms or strong wave-absorbing material. This article introduces a novel method utilizing Dempster–Shafer (DS) theory augmented by least squares support vector machines (LSSVMs) known as DS-LSSVM. The SINS and DVL data fusion are designed by DS theory whereas LSSVM models the SINS error. The virtual DVL is built by the proposed DS-LSSVM method, which makes AUV navigation purposes possible during the long-term DVL outage. The test results demonstrate the effectiveness of the proposed virtual DVL signal estimation method. In addition, the positioning accuracy of the proposed method outperforms that of the LSSVM method.

In order to solve the problem of SINS/DVL/USBL system interference in complex underwater environment. An asynchronous sequential robust filter method is proposed in this paper. The original output azimuth, slant range and altitude of the ultra-short baseline as the measurement information, a compact combination model is established. The problem of large amount of computation caused by the high measurement dimension of multi-sensor information fusion is solved by introducing asynchronous sequential filter technology. Meanwhile, a robust Kalman filter algorithm is proposed, which based on Mahalanobis distance. The outlier detection is designed based on statistical characteristics. Finally, the effectiveness of the method proposed in this paper is verified by simulation and River experiments. The experimental results show that the method proposed in this paper can effectively eliminate outliers and improve the navigation accuracy while reducing the amount of computation.

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.

In navigation of an autonomous underwater vehicles (AUVs), estimation of position is an important issue, especially when the sensors such as gyroscopes contain a lot of noise and the velocity information of Doppler Velocity Log (DVL) is affected by the motion attitude of the vehicle. In this paper, based on an improved auto regressive (AR) model, a real-time filter is utilized for Gyroscope signal de-noising. Meanwhile, according to the characteristics of the AUV, the influence of the vehicle attitude on the DVL velocity measurement error is analyzed and a motion attitude assist (MAA) method based on error model is introduced for enhancing DVL velocity accuracy. In this study, using the proposed hybrid approach, an inertial navigation system (INS)/DVL integrated navigation system is designed. The proposed approach is evaluated by simulation and experimental test in different acceleration bound and existence of DVL outage for an AUV. The results indicate that the precisions of the velocity and position are improved effectively, especially in complex motion attitude and long sailing conditions.