Showing papers by "Tao Zhang published in 2020"

A Novel SINS/DVL Tightly Integrated Navigation Method for Complex Environment

Abstract: 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 does not work when some beam measurements are inaccurate or outages for complex underwater environment. To solve these problems, in this article, a novel tightly integrated navigation method composed of an 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 a complex environment. Compared with loosely integrated method, the position accuracy of the proposed method has improved by 32.5%.

Student’s t-Based Robust Kalman Filter for a SINS/USBL Integration Navigation Strategy

Abstract: In order to satisfy the requirements of the placement, the operation, and the high-precision navigation and positioning for the underwater vehicles and the underwater operational platform, a SINS/USBL integration navigation strategy is proposed. This paper presents a robust Student’s t-based Kalman filter for strap-down inertial navigation system and ultra-short base line (SINS/USBL) integration system, which is proposed to suppress the measurement uncertainty induced by the acoustic outliers. Firstly, a SINS/USBL integration prototype system is designed and presented, which is constructed by an inertial measurement unit (IMU) and an USBL acoustic array in an inverted configuration, and they can be entirely designed and developed in-house. Furthermore, an improved robust Student’s t-based Kalman filter with the degree of freedom (dof) parameter is proposed to better address the acoustic outliers in the measured range and directions information, the heavy-tailed measurement noise induced by the acoustic outliers can be modelled as a Student’s t distribution, the posterior probability density functions (PDFs) of the state variable, the auxiliary random variable and the dof parameter are updated as Gaussian, Gamma, and Gamma prior PDF, respectively, and the corresponding statistics and the state vector are jointly inferred using the variational Bayesian (VB) approach. Finally, based on the state error equations and the derived measurement equation of SINS/USBL integration navigation system, the mathematical simulation test and the field trial are performed to demonstrate the feasibility and the superiority of the proposed SINS/USBL integration approach.

A Fast Robust In-Motion Alignment Method for SINS With DVL Aided

Abstract: In this paper, a fast robust in-motion alignment method is proposed for the strapdown inertial navigation system (SINS) with DVL aided. The proposed method is divided into two procedures, which are coarse alignment procedure and fine alignment procedure. In the coarse alignment procedure, an apparent velocity modeling method is investigated. Building upon this model, a robust Kalman filter (RKF) is designed for parameter estimation, then an optimal observation vector, in which the outliers of the Doppler Velocity Log (DVL) outputs are eliminated, is reconstructed. As compared with the existing method, in the proposed method, the robustness of coarse alignment procedure is enhanced. To improve the accuracy of the coarse alignment method, a fine alignment method is designed. Different from the traditional fine alignment method, the error model of the proposed fine alignment method is constructed in n 0-frame. With this advantage, the time-varying error attitude estimation for the traditional method has transformed into a time-invariant error attitude estimation. Thus, the raw data and intermediate data, which are collected in the coarse alignment procedure, can be reused in the fine alignment procedure. Take advantage of the high performance of the navigational computer, the forward-forward data processing can be carried on repeatedly, the one cycle of the forward-forward procedure consumes about 0.3 s in the real-time system. Simulation and field tests showed that the proposed method can obtain a high-accuracy initial alignment results and suppress the interference of the outliers of the DVL outputs.

Robust Initial Alignment for SINS/DVL Based on Reconstructed Observation Vectors

Abstract: 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 article, 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. First, the alignment principle for SINS/DVL is summarized. Second, 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, 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.

A Calibration Method of USBL Installation Error Based on Attitude Determination

Abstract: 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.

A Fast SINS Self-Alignment Method Under Geographic Latitude Uncertainty

Abstract: To achieve high alignment accuracy of strapdown inertial navigation system (SINS) with the limited time and the unknown geographic latitude constraints, a novel fast self-alignment method with the real-time estimated latitude is proposed. In the stage of the coarse alignment, the latitude self-estimation and the coarse alignment are performed at the same time. The geometric relationship between the transition angle and the latitude is established, where the angle can be determined by the gravity acceleration vector at different times. Based on the reconstructed observation vector and the set dynamic window, a real-time latitude self-estimation algorithm is proposed for a swaying case, and the attitude determination procedure is accomplished by the improved filter quaternion estimator (filter-QUEST) algorithm, where the vectors are constructed based on a generalized integration regulation. With the deduced backtracking alignment error model, the forward fine alignment is performed with the statistical latitude estimate and the saved data during the process of the coarse alignment, thus will obviously speed up the overall alignment time. The results of the mathematical simulation and the turntable experiment are performed to validate the estimation and alignment performance of the proposed approach.

A High-Order Coning Error Compensation Algorithm Under High Rate Maneuvering

Abstract: High-precision strapdown inertial navigation system (SINS) is of vital importance for a variety of applications. The attitude performance of SINS needs to be paid special attention especially when the platforms work under high rate maneuvering. The traditional method is based on the rotation vector algorithm by leveraging multiple samples of gyro measurements. However, the accuracy of the multiple samples algorithm is limited as the higher-order term error has not been taken into consideration. In the paper, a new high-order coning error compensation algorithm is proposed for strapdown inertial navigation system. A general algorithm for the high-order Picard component of the rotation vector differential equation is presented and a simplified correction structure of the high-order non-commutativity error is given. Simulations under coning vibrational environments showed that the proposed algorithm has higher attitude accuracy under highly dynamic vibrational conditions compared with the existing methods. Thus, the new high-order attitude updating algorithm can improve the accuracy of inertial navigation.

A Fast Calibration Method for Dynamic Lever-Arm Parameters for IMUs Based on the Backtracking Scheme

Abstract: Calibration is vital to improving the accuracy of the strapdown inertial navigation system (SINS). Calibration is one of the crucial phases before the operation of SINS and requires high accuracy and speed. Traditional system-level calibration methods have the disadvantages of long calibration times and complicated path design. A fast calibration method for the lever arm and other error parameters is proposed in this article. To shorten the calibration time and improve the calibration accuracy, a backtracking calibration method in inertial frames based on the reduced-order Kalman filter is proposed. Simulations and experiments are given to illustrate the effectiveness of the novel calibration method. The calibration process can be completed in 10 min, which is greatly shortened compared with traditional methods. The calibration time is shortened, and the calibration accuracy is improved. Furthermore, navigation experiments show that the velocity and position accuracies of the SINS are improved significantly after compensation with the calibration results, fully illustrating the significance of the proposed calibration method in improving the navigation performance.

Unmanned ship global path planning method based on improved A star algorithm

Abstract: The invention provides an unmanned ship global path planning method based on an improved A star algorithm, which relates to the field of path planning. According to the method, a traditional A star algorithm neighborhood search strategy is changed, a search neighborhood is expanded, a traditional heuristic function is improved, a deweighting strategy is combined, and an angle factor is added, so that a result obtained during path search is distributed near a connecting line of a starting point and a target point in a biased manner, and the search efficiency is improved. According to the method, the marine geographic information is acquired by utilizing an electronic chart, the improved A star algorithm is combined with a dynamic grid method, the grid map model is constructed through grid dynamic refinement, the improved A star algorithm is applied to search for the path, the path precision gradually meets the precision requirement, and redundant path nodes are further reduced through smooth path processing.

Research on the Calibrated Method for MEMS Magnetometer Arrays

Abstract: The MEMS magnetometer determines the orientation for the MEMS inertial system. Because of the large noise of the MEMS magnetometer and the interference of soft and hard iron outside, the measurement error of the MEMS magnetometer is large. To reduce the effects of the random noises, the MEMS magnetometer arrays are designed in this paper. In our design, thirty-two MEMS magnetometers are welding on a printed circuit board (PCB), which area is 5×5 cm2. The forty general-purpose input-output (GPIO) ports, which are thirty-two data ports and eight clock ports, are used to collect the data of MEMS magnetometers. Then, averaging the thirty-two measurements of the MEMS magnetometers, the random noises of the measurements of the MEMS magnetometers can be reduced. Based on the averaging operation for the collected sensors' data, a unified measurement model for the MEMS magnetometer arrays is constructed. Using the unified measurement model, an adaptive Kalman filter is developed to estimate the unknown parameters. To validate the performance of the MEMS magnetometer arrays, the simulation and experimental tests are designed. The test results show that, comparing with the single MEMS magnetometer, the random noises of the MEMS magnetometer arrays are reduced effectively.

Robust Initial Alignment for SINS/DVL Based on Reconstructed Observation Vectors.

Abstract: 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.