To improve the efficiency of the calibration process and enhance the adaptability of the calibration method, an interacting multiple model and unscented Kalman filter (IMM-UKF) aided strapdown inertial navigation system (SINS) / Ultra-Short Base Line (USBL) calibration solution is proposed for the common calibration procedure, where only one transponder is employed without pre-locating. Firstly, construct the SINS/USBL calibration mechanism, where the transponder's position, level-arm and misalignment angle are estimated simultaneously utilizing the slant range, inclination angles of USBL and depth information from depthometer. Second, to mitigate the decreasing calibration accuracy caused by the single filtering parameter under the complex underwater environment, an IMM-UKF algorithm is presented to support the proposed calibration mechanism. Simulation results indicate that the proposed mechanism earns faster convergence rate and better calibration results than other solutions. Besides, the proposed solution can maintain its robustness when the observation quality changes.

By analyzing the error models of the measured vectors of the gravitational apparent motion, an in-motion filter-QUEST alignment method only with the inertial measurement unit is presented in this paper. The contribution of the proposed method lies in constructing the in-motion model of the measured vectors of the gravitational apparent motion and developing the extracted method to reconstruct the measured vectors. Furthermore, the relationship between the noise characteristic and the moving state of the vehicle is analyzed in detail. Different from the several current techniques, the presented method can be carried out without any other external additional equipment, when the vehicle is in-motion. With the designed real-time wavelet denoising (RWD) method, the high-frequency noises of the measured vectors are filtered. Based on the constructed parameter recognition model of the measured vectors, a robust adaptive Kalman filter is devised to estimate the optimal parameters, which are used to calculate the reconstructed observation vectors. Moreover, the gross outliers, which are contained in the filtered vectors of the RWD, are eliminated effectively. The simulation and the field trial results demonstrate that the presented method is applicable to the in-motion initial alignment, and it can serve as a nice initial alignment method in the follow-on fine alignment process and navigation process.

Misalignment angle will result in a considerable error for the integration of Doppler Velocity Log (DVL) and of Strapdown Inertial Navigation System (SINS). In this paper, a robust initial alignment method for SINS/DVL is proposed to solve a practical applicable issue, which is that the outputs of DVL are often corrupted by the outliers. Firstly, the alignment principle for SINS/DVL is summarized. Secondly, based on the principle of this alignment method, the apparent velocity model is investigated, and the parameters expression of the apparent velocity model are derived detailed. Using the apparent velocity model, the unknown parameters of the apparent velocity model are estimated by the developed Robust Kalman Filter (RKF), then the reconstructed observation vector, where the outliers are detected and isolated, is reconstructed by the estimated parameters. Based on the reconstructed observation vectors, the initial attitude is determined. Finally, the simulation and field tests are carried out to verify the performance of the proposed method. The test results are shown that the proposed method can detect and isolate the outliers effectively and get better performance than the previous work.

This article addresses the underwater acoustical localization problem by using the time-difference-of-arrival (TDOA) and bearing-angle-of-arrival (BAOA) measurements. For the underwater acoustic equipment, such as the ultrashort baseline system (USBL), whose bearing measurements are different from the traditional angle-of-arrival (AOA) model, a closed-form solution for the hybrid TDOA/BAOA-based source localization problem is developed. However, the solution suffers from the measurement noise and cannot achieve the Cramer–Rao lower bound (CRLB) performance in the case of large measurement noise. Thus, an iterative constrained weighted least-squares method is presented to further minimize the error in the case of large noise. The CRLB for hybrid TDOA/BAOA source localization is analyzed, and the solution is proved to achieve the CRLB performance. Numerical simulations and field tests demonstrate that the proposed method outperforms the traditional methods in terms of estimation bias and accuracy. It can achieve the CRLB performance better.

Misalignment angle is an important error in the integration of the Strapdown Inertial Navigation System (SINS) and the Global Positioning System (GPS). Currently, the popular methods for SINS/GPS integrated system are based on the ground velocity. There are two major defects: (1) not all GPS receivers provide the velocity outputs; (2) the contained outliers in the velocity will degrade the performance of the alignment methods. In this paper, an in-motion coarse alignment method for SINS/GPS integrated system using position loci is proposed. Firstly, the observation vector for the in-motion coarse alignment with the position loci is derived. Different from the current popular methods, the constructed observation vector is just relative with the GPS positioning information, which can be obtained from any GPS receiver directly. Thus, the applicable range of the in-motion coarse alignment method is extended. Secondly, based on the advantages of the position loci, the proposed method can suppress the interferences of the outliers, which are contained in the GPS positioning outputs. Finally, the simulation and field tests are designed to verify the performance of the proposed method. The test results show that the proposed method is more robust than the current popular methods.