Yao Li

In this paper, the in-motion polar alignment method for SINS/GPS integration is proposed. Firstly, to address the defects of the fast convergence of the longitude with the navigational mechanization, the unified navigation mechanization is used to construct the vector observations for the in-motion alignment method. Secondly, the initial velocity errors, which are generated by the GPS outputs, are considered. To suppress the initial velocity errors, the improved vector observation with the vector subtraction improvements is investigated. With the vector subtraction improvements, the initial velocity errors are eliminated efficiently. Thirdly, to enhance the correction of the observation vectors for the attitude determination the continuous attitude determination method with the expansion samples of the GPS outputs is designed. Lastly, to verify the performance of the proposed method, the virtual polar region technology in the transverse mechanization is devised. The simulation and field tests are designed to verification, the test results are shown that the proposed method can finish the in-motion polar alignment process, and the initial velocity errors are suppressed. Moreover, the subtraction operation for observation vectors do not degrade the performance of the alignment process.

In order to meet the needs of high-precision navigation for underwater vehicles, a strapdown inertial navigation system (SINS)/ultra-short baseline (USBL) tightly coupled integrated strategy is proposed, which can avoid the errors generated in the calculation of absolute position and improve the positioning accuracy of the integrated navigation system compared with the loosely coupled strategy. In addition, due to the influence of environmental noise in the ocean, the measurement of USBL is accompanied by a large number of outliers, which leads to the decrease of the precision of the integrated navigation system based on traditional Kalman filter. Therefore, a robust filter based on Gaussian-Exponential distribution is presented for the SINS/USBL integrated navigation system in this paper. Finally, the feasibility and the superiority of the proposed algorithm is verified through simulation and field test in the Yangtze River.

The strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) are the major positioning system for underwater vehicles. The initial alignment process is the first working stage of SINS/DVL integration. Currently, the initial alignment method for SINS/DVL integration is imperfect. The major problems can be summarized in two aspects. One is the initial velocity errors and outliers of DVL outputs, which are not suppressed simultaneously. The other is the positioning uncertainty when the attitude alignment is finished. To address these two problems, robust attitude and positioning alignment methods are proposed in this article. First, a vector observation, which is based on the sliding windows improvements, is constructed for eliminating the initial velocity errors. Second, the apparent velocity motion model for the vector observation with the sliding window improvements is constructed. Based on the constructed model, a parameter estimation method, which is established by a robust Kalman filter, is proposed. Using the estimated parameters, the new observed vectors are reconstructed. Thus, the robust attitude alignment process is finished. Third, real-time and postprocessing positioning alignment methods are proposed for addressing positioning uncertainty. At last, the simulation and field tests are designed for verifying the performance of the proposed method. The test results are shown that the existing two problems are overcome.

In this paper, an improved in-motion alignment method for SINS/GPS integration is proposed. The alignment errors, which are caused by the maneuvering of the vehicle and the accumulated errors of inertial sensor errors, are suppressed. Firstly, the traditional in-motion alignment method for SINS/GPS integration is reviewed, and the existing problems, which are accumulated errors in the observed vectors, are investigated. To address these problems, a sliding windows operation for vector observation optimization is proposed. The derivation for the calculation of the new vector observations is given in detail. Using the new vector observations, the accumulated errors, which are existing in the observed vectors, are weakened. Thus, the alignment results are more stable whenever any maneuvering of the vehicle. Lastly, the simulation and field tests are designed for verification of the proposed method. Compared with the currently popular methods, the alignment errors are converged to a small value quickly.

In the conventional hull deformation measurement method, the external measurement noise is considered constant. However, due to the complex sea conditions and the high-frequency chatter caused by the waves, the measurement noise is colored noise. The aim of this paper is to develop a hull deformation measurement method based on fiber optic gyro (FOG) angular rate matching in complex sea conditions. On the one hand, the traditional system model is optimized to reduce the dimension, and a simplified linear state space model is proposed. On the other hand, a finite impulse response (FIR) low-pass digital filter is designed and introduced into the angular rate matching measurement method, so that the deformation estimation accuracy can still be maintained in complex sea conditions. The simulation and swing table test are designed to verify the effectiveness of the hull deformation measurement method proposed in this paper. The results show that the proposed method in this paper can effectively eliminate external noise interference and improve estimation accuracy.

To deal with the problems of outliers and nonlinearity in the complex underwater environment, a Huber’s M-estimation-based cubature Kalman filter (CKF) is proposed for an inertial navigation system (INS)/Doppler velocity log (DVL) integrated system. First, a loosely coupled INS/DVL integrated system is designed, and the nonlinear system model is established in the case of big misalignment angle. Then, Huber’s M-estimation is introduced for robust estimation to resist outliers. Meanwhile, the CKF is focused to handle the nonlinearity of the state equation. Finally, simulation and the vehicle test are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the conventional Kalman filter (KF) and outlier detection-based robust cubature Kalman filter (RCKF) in terms of navigation accuracy in the complex underwater environment.

The Ultra-short baseline (USBL) positioning system has important application in the positioning of underwater vehicles. The installation error angle of the USBL positioning system has an important influence on the positioning accuracy of USBL system. The traditional calibration methods have limited estimation accuracy for installation error angles and have high route requirements. To solve the above problems, a calibration method of installation error angle based on attitude determination is proposed in this paper. When strapdown inertial navigation system (SINS) and USBL are fixed together in the application process, the installation error angle of USBL is fixed and unchanged. Then the calibration of installation error angle can be accomplished with the idea of attitude determination. The vector observation model based on the installation error angle matrix is established first. Observation vectors are obtained by the relative position of transponders in the USBL coordinate frame. The reference vector is calculated by position of transponder, position and attitude of SINS and lever arm between SINS and USBL. By constructing the observation vectors and the reference vectors, the proposed method can calibrate the installation error angle of SINS and USBL in real time. The advantages of the proposed method are that it has no specific requirements for the calibration route and can calibrate the installation error angle in real time with high accuracy. In order to verify the performance of the proposed algorithm, simulation experiment and field experiment are carried out in this paper. The results of simulation experiment and field experiment show that the proposed method can give the estimated installation error angle of USBL in real time, and the estimated result is the best among several methods. The proposed method can not only achieve the calibration of the installation error angle in circular trajectory, but also in straight trajectory.

The USBL (Ultra-Short Base Line) positioning system is widely used in underwater acoustic positioning systems due to its small size and ease of use. The traditional USBL positioning system is based on ‘slant range and azimuth’. The positioning error is an increasing function with the increase in distance and the positioning accuracy depends on the ranging accuracy of the underwater target. This method is not suitable for long-distance underwater positioning operations. This paper proposes a USBL positioning calculation model based on depth information for ‘rotating array and reusing elements’. This method does not need to measure the distance between the USBL acoustic array and target, so it can completely eliminate the influence of long-distance ranging errors in USBL positioning. The theoretical analysis and simulation experiments show that the new USBL positioning model based on ‘rotating array and reusing elements’ can completely eliminate the influence of the wavelength error and spacing error of underwater acoustic signals on the positioning accuracy of USBL. The positioning accuracy can be improved by approximately 90%, and the horizontal positioning error within a positioning distance of 1000 m is less than 1.2 m. The positioning method has high precision performance in the long distance, and provides a new idea for the engineering design of a USBL underwater positioning system.

The angle misalignment error of a USBL (Ultrashort Baseline) acoustic array is one of the major error sources of the strapdown inertial navigation system/USBL positioning system, which will directly affect the positioning accuracy of the USBL positioning system. For the traditional calibration method cannot accurately estimate the angle misalignment error due to its strict trajectory requirements in the field experiment and the high-precision layout of the transceiver array elements, a new method for estimating the angle misalignment error of a USBL acoustic array based on single transponder and dual-vector reconstruction is studied in this paper. The precondition of USBL misalignment calibration is to locate the underwater transponder accurately. In this paper, the single transponder segmentation iterative long baseline method is used to locate the underwater target transponder. The dual-vector reconstruction method is studied to control the estimation accuracy of USBL misalignment error calibration based on the traditional single transponder method, which provides a theoretical basis for the determination of the iteration times to the USBL angle misalignment error estimation module. The underwater experiment results show that the positioning error could be reduced to less than 1 m after the angle misalignment error compensation. The underwater transponder positioning and the angle misalignment error estimation of USBL could be accomplished in a circle sailing. It is a new method with good performance of high estimation accuracy, simple operation, and easy realization.

In this paper, a coarse alignment method based on apparent gravitational motion is proposed. Due to the interference of the complex situations, the true observation vectors, which are calculated by the apparent gravity, are contaminated. The sources of the interference are analyzed in detail, and then a low-pass digital filter is designed in this paper for eliminating the high-frequency noise of the measurement observation vectors. To extract the effective observation vectors from the inertial sensors’ outputs, a parameter recognition and vector reconstruction method are designed, where an adaptive Kalman filter is employed to estimate the unknown parameters. Furthermore, a robust filter, which is based on Huber’s M-estimation theory, is developed for addressing the outliers of the measurement observation vectors due to the maneuver of the vehicle. A comprehensive experiment, which contains a simulation test and physical test, is designed to verify the performance of the proposed method, and the results show that the proposed method is equivalent to the popular apparent velocity method in swaying mode, but it is superior to the current methods while in moving mode when the strapdown inertial navigation system (SINS) is under entirely self-contained conditions.