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%.

To enhance the performance of the navigation system under the complex underwater environment, a hybrid interacting multiple model (HIMM) aided inertial navigation system (INS)/Doppler velocity logs (DVL) integration solution is proposed. First, to employ the acoustic Doppler current profiler mode of DVL, a novel INS/DVL mechanism is constructed where the water current velocity is estimated in real time and both bottom-track and water-track velocity measurements of DVL are involved in the observation vector. Meanwhile, to deal with the outliers and observation noises in the DVL's measurements, a HIMM algorithm is proposed to adaptively select the proper models to describe the changing environment. Simulations and field test are conducted to evaluate the effectiveness of the proposed algorithm, where the interacting multiple model (IMM) algorithm is employed for comparison. The results indicate that the proposed HIMM-aided INS/DVL integration solution shows superiority than the traditional IMM method when the observation noises and outliers exist. Meanwhile, the proposed integration scheme can successfully bridge the DVL's bottom-track outages.

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.

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.

Finding the position of a radiative source based on time-difference-of-arrival (TDOA) measurements from spatially separated receivers has been widely applied in sonar, radar, mobile communications and sensor networks. For the nonlinear model in the process of positioning, Taylor series and other novel methods are proposed. The idea of cone constraint provides a new way of solving this problem. However, these approaches do not always perform well and are away from the Cramer-Rao-Lower-Bound (CRLB) in the situations when the source is set at the array edge, the noise in measurement is loud, or the initial position is biased. This paper presents a weighted-least-squares (WLS) algorithm with the cone tangent plane constraint for hyperbolic positioning. The method adds the range between the source and the reference sensor as a dimension. So, the space-range frame is established. Different from other cone theories, this paper sets the reference sensor as the apex and finds the optimal source estimation on the cone. WLS is used for the optimal result from the measurement plane equations, a vertical constraint and a cone constraint. The cone constraint equation is linearized by a tangent plane. This method iterates through loops and updates the tangent plane, which approximates the truth-value on the cone. The proposed algorithm was simulated and verified under various conditions of different source positions and noises. Besides, some state-of-the-art algorithms were compared in these simulations. The results show that this algorithm is accurate and robust under poor external environment.

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.