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This paper proposes an improved adaptive filtering algorithm based on the Sage–Husa adaptive Kalman filtering algorithm to address the issue of measurement noise characteristics impacting the navigation accuracy in strapdown inertial navigation system (SINS)/Doppler Velocity Log (DVL) integrated navigation systems. Addressing the non-positive definite matrix problem prevalent in traditional adaptive filtering algorithms and aiming to enhance measurement noise estimation accuracy, this method incorporates upper and lower thresholds determined by a discrimination factor. In the presence of abnormal measurement data, these thresholds are utilized to adjust the covariance of the innovation, subsequently re-estimating the system's measurement noise through a decision factor based on the innovation. Simulation and experiment results demonstrate that the proposed improved adaptive filtering algorithm outperforms the classical Kalman filter (KF) in terms of navigation accuracy and stability. Furthermore, the filtering performance surpasses that of the Sage–Husa algorithm. The simulation results in this paper show that the relative position positioning error of the improved method is reduced by 49.44% compared with the Sage–Husa filtering method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For autonomous undersea vehicle’s requirement of high-precision and high-reliability navigation and positioning in complex underwater environments, a tightly integrated navigation algorithm involving strapdown inertial navigation system(SINS)/Doppler velocity log(DVL)/ultra-short baseline(USBL) positioning system was proposed. A SINS/DVL tightly integrated measurement equation based on frequency shift measurement and a SINS/USBL tightly integrated measurement equation based on relative measurement information were established. SINS, DVL, and USBL information were fused using a concentrated Kalman filter. In view of the decline of navigation accuracy caused by the complex underwater environment, the DVL and USBL data outliers were fully considered, and the fault data were detected and isolated by Chi-square detection. In addition, the measurement equation dimensions were updated in real time to ensure the precision of the system. The simulation results show that the proposed algorithm has higher positioning precision than other integrated model algorithms. Compared with the traditional SINS/DVL/USBL concentrated filtering method based on velocity measurement and relative position measurement, the precision can be improved by about 23%. In the case of DVL data failure, the positioning error only increases by 5.2% compared with the normal condition. In the case of USBL data failure, the positioning error increases by 165.4% compared with the normal condition, and the robustness and stability are significantly better than the SINS/DVL/USBL concentrated filtering navigation algorithm based on other measurements. Therefore, it can achieve high-precision and high-reliability underwater navigation and positioning.

This paper proposes an improved adaptive filtering algorithm based on the Sage–Husa adaptive Kalman filtering algorithm to address the issue of measurement noise characteristics impacting the navigation accuracy in strapdown inertial navigation system (SINS)/Doppler Velocity Log (DVL) integrated navigation systems. Addressing the non-positive definite matrix problem prevalent in traditional adaptive filtering algorithms and aiming to enhance measurement noise estimation accuracy, this method incorporates upper and lower thresholds determined by a discrimination factor. In the presence of abnormal measurement data, these thresholds are utilized to adjust the covariance of the innovation, subsequently re-estimating the system’s measurement noise through a decision factor based on the innovation. Simulation and experiment results demonstrate that the proposed improved adaptive filtering algorithm outperforms the classical Kalman filter (KF) in terms of navigation accuracy and stability. Furthermore, the filtering performance surpasses that of the Sage–Husa algorithm. The simulation results in this paper show that the relative position positioning error of the improved method is reduced by 49.44% compared with the Sage–Husa filtering method.

The paper presents the analytical review of an inertial measuring unit (IMU) rotation as a method to improve the accuracy of a strapdown inertial navigation system (SINS). There are two ways to improve the accuracy. One of them is based on the transformation of the error change pattern in the inertial sensors (IS) when using the IMU self-compensation rotation (SCR). The criteria for selecting an efficient SCR law to minimize the accumulated error in the parameters generated by SINS are presented. Along with the advantages of this technology, its weak points that may limit significantly the potentially achievable IMU accuracy are described. The other technique consists in increasing the observability of the IS error model components due to the IMU rotation while filtering the SINS errors. The IS error model is described, and the problem of recursive filtering of the SINS errors is stated to refine these errors, with the reference data on coordinates and motion velocity being available. The methods for quantifying the observability of the IS error model components are presented. [ABSTRACT FROM AUTHOR]

This paper aims to assess the possibility of using functionally redundant inertial units to solve problems of increasing reliability and ensuring the fault tolerance of the various classes and purposes of aircraft navigation systems. We present the results of studying failure detection methods to improve the accuracy and reliability of a strapdown functionally redundant inertial unit. The resulting structural redundancy of the strapdown inertial measurement unit is designed to increase the fault tolerance and accuracy of strapdown inertial navigation systems. The methods for detecting sensor failures in functionally redundant inertial units are based on the use of the equations of functionally redundant inertial unit compliance to nominal requirements for the accuracy of measuring the input action vector. To describe the methods for detecting and eliminating failed sensor and algorithm designs based on them, we gave the mathematical models of the measurement vector of functionally redundant inertial units concerning the measured vector and the error identification condition, including the residual of the matching equations with the size due to the level of redundancy, determining the total number of matching equations. The main criterion for determining a failed sensor is non-compliance with the nominal value of the residual included in a certain number of matching equations of the information received from such meters. The developed algorithms are examined using simulation methods. The study of the selected structure of the functionally redundant inertial units shows that the proposed approaches are efficient. Also, we manage to identify the main characteristics of the algorithms for detecting sensor failures that are structurally a part of the functionally redundant inertial units. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of Nanjing University of Aeronautics & Astronautics is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In coal mines, accurate positioning is vital for roadheader equipment. However, most roadheaders use a standalone strapdown inertial navigation system (SINS) which faces challenges like error accumulation, drift, initial alignment needs, temperature sensitivity, and the demand for high-quality sensors. In this paper, a roadheader Visual–Inertial Odometry (VIO) system is proposed, combining SINS and stereo visual odometry to adjust to coal mine environments. Given the inherently dimly lit conditions of coal mines, our system includes an image-enhancement module to preprocess images, aiding in feature matching for stereo visual odometry. Additionally, a Kalman filter merges the positional data from SINS and stereo visual odometry. When tested against three other methods on the KITTI and EuRoC datasets, our approach showed notable precision on the EBZ160M-2 Roadheader, with attitude errors less than 0.2751° and position discrepancies within 0.0328 m, proving its advantages over SINS. [ABSTRACT FROM AUTHOR]

To improve the reliability of strapdown inertial navigation system (SINS)/Doppler radar/odometer integrated navigation system, the federated Kalman filter with two-stage fault detection structure is designed, and a fault-tolerant SINS/Doppler radar/odometer integrated navigation method is proposed. Firstly, the pre-fault detection module sets before the local filter, and the residual chi-square test in the carrier coordinate system is selected to detect the abrupt faults of Doppler radar and odometer. Then, the secondary-fault detection module emplaces between the local filter and the main filter, and the sequential probability ratio test (SPRT) is selected to further detect the ramp faults that are difficult to detect by the residual chi-square test. To address the limitation of the SPRT in accurately determining the end time of faults, an improved SPRT is proposed. The improved SPRT reduces the influence of historical fault on the fault statistics by introducing forgetting factors to improve its sensitivity to the fault end. The simulation experiment indicates that the proposed method can quickly detect and isolate abrupt and ramp faults, and promptly restore normal operation of the integrated navigation system after the fault ends, effectively improving the fault tolerance and reliability of the integrated navigation system. [ABSTRACT FROM AUTHOR]

To study the accuracy characteristics, a strapdown inertial navigation system is represented as a set of kinematic equations and equations of a mathematical model of the Earth. Based on the mathematical model in the Matlab-Simulink package, a modeling complex was created, consisting of subsystems of the reference and studied navigation system, subsystems of the reference and simplified model of the Earth and a subsystem of primary information sensors. In navigation subsystems, kinematic equations of inertial navigation algorithms are solved, and matrices of direction cosines are formed. In the subsystems of the Earth model, the parameters of the Earth's spheroid and the acceleration of gravity are calculated. The sensor models are developed based on the characteristics of low-cost microelectromechanical sensors. The purpose of the study was to assess methodological and instrumental errors as the difference in the dead reckoning signals of the flight navigation parameters of the reference and studied navigation systems. Methodological errors of the system are played up by simplifications of the parameters of the earth's spheroid, and instrumental errors are caused by errors in inertial sensors. [ABSTRACT FROM AUTHOR]

The problem of angular orientation of a moving object is solved using measurements of the strapdown inertial navigation system (SINS) and the satellite navigation system (SNS) under the most general assumptions about the character of the angular motion of the object and probabilistic characteristics of noises in inertial measurements. The soltion is obtained in the form of an extended Kalman filter in which the direction cosines of rotation in an inertial coordinate system are used as estimated orientation parameters and the vector of measurements by the SNS and SINS accelerometers is used as the observation vector. An example illustrating the high stability and convergence rate of the process of estimating the chosen parameters of angular orientation of the moving object is presented. [ABSTRACT FROM AUTHOR]

Aiming at the in-motion initial alignment problem of strapdown inertial navigation system (SINS), this paper proposed a precise direct in-motion alignment method based on attitude matrix representation and Cayley–Kalman filter on the special orthogonal group. There are two innovations in this paper. First, as an element of the special orthogonal group S O (3) , the error attitude matrix is used to construct an in-motion alignment model, and it is transformed into the vector space to represent the attitude error state. The error matrix was used as a state to decrease the influence of the state-dependent problem. Second, this method uses the Cayley transform to replace the Taylor expansion to derive the update equation, which is a no approximation mapping relation to assure accuracy when deriving the filtering algorithm. Simulation and experimental results demonstrate the proposed alignment method has advantages over the existing methods in alignment accuracy and alignment time and performs well in the in-motion alignment process of SINS. [ABSTRACT FROM AUTHOR]

The major challenge of current strapdown inertial navigation system/celestial navigation system (SINS/RCNS) is failing to calibrate all the error coefficients of gyro and star sensor in-flight accurately, which will lead to the attitude accuracy degradation. To address this question, this paper proposes an optimal calibration method based on observability analysis. Here, we derive the star sensor measurement model with respect to initial alignment errors, gyro errors, and star sensor installation errors. And the observability analysis is employed to explain the reason why all the error coefficients can be estimated effectively by three maneuvers merely. Finally, the optimal observation sequence is determined based on the fisher information matrix (FIM). The proposed optimal calibration method is evaluated by a representative suborbital flight vehicle trajectory, which represents significant improvements in estimation accuracy of error coefficients, and realizes the attitude accuracy enhancements. [ABSTRACT FROM AUTHOR]

The article proposes the approaches to updating a strapdown inertial navigation system (SINS) based on data of the airborne electro-optical system (EOS) of an unmanned aerial vehicle (UAV). It is specified that the EOS is presented as a navigation data sensor. The rationale for the feasibility of such an approach is formed, especially in the terms of signal lack or suppression of satellite radio-navigation systems. It is proposed to ensure the accuracy of self-contained navigation by assigning an UAV route, including waypoints with terrestrial references (TRs). Notably, TR-associated image information is preliminarily downloaded into the flight management computer (FMC). The automated TR identification system with denoted coordinates at next waypoints, using airborne data, in fact, allows for alternative global positioning. The reliable operation of such an integrated navigation system over sufficiently extended legs of flight path, first, depends on the accuracy of its constituent elements. Taking into consideration the fact that conventional sensors of navigation information, such as a SINS and an altimeter, are quite well studied in numerous contributions. The article focuses on the UAV airborne electro-optical system and, specifically, on its application features as a navigation sensor. The factors influencing the accuracy of the UAV positioning data determination at waypoints according to the data of the airborne EOS are considered. The developed mathematical model of errors for the UAV inertial optical navigation complex (IONC) is presented. The analysis of the impact of airborne altimeter inaccuracies, earth’s surface features and the shift of the onboard digital camera optical axis, caused by random evolutions of the carrier body in turbulent atmosphere on the positioning accuracy, was conducted. The results of calculating lapses in determining the UAV positioning data, equipped with IONC, are given.

With the improvement of the accuracy requirements of high-precision long endurance inertial navigation system, the gravity disturbance has become the main error source of inertial navigation system.Is the key factor to improve the navigation accuracy whether it can be effectively compensated. The traditional compensation method based on spherical harmonic model cannot reflect the detailed information of the earth’s gravity field, and thus has poor compensation result for medium and short wave gravity disturbance components. In the traditional state estimation method, the Markov state model has poor accuracy in describing the long wave gravity disturbance component, so the compensation result of the long wave component is poor. The above methods can not compensate the actual gravity disturbance that has wide band with high precision. To solve this problem, a high-precision gravity disturbance compensation method based on spherical harmonic model and multi-sensor information fusion is designed in this paper. On the one hand, the method compensates the long wave gravity disturbance component by using the high calculation accuracy of the low-order spherical harmonic model in the long wave band. On the other hand, using strapdown inertial navigation/laser Doppler velocimetry/barometric altimeter to form a fully autonomous integrated navigation system, the residual medium and short wave gravity distur-bance component is established as a high-precision Markov model, so as to realize the state estimation and compensation of medium and short wave component. The simulation results show that the proposed gravity distur-bance compensation method can effectively improve the estimation effect of gravity disturbance, realize high-precision gravity disturbance compensation and have high navigation accuracy.

Underwater vehicles are key carriers for underwater inspection and operation tasks, and the successful implementation of these tasks depends on the positioning and navigation equipment with corresponding accuracy. In practice, multiple positioning and navigation devices are often combined to integrate the advantages of each equipment. Currently, the most common method for integrated navigation is combination of the Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL). Various errors will occur when SINS and DVL are combined together, such as installation declination. In addition, DVL itself also has errors in the measurement of speed. These errors will affect the final accuracy of the combined positioning and navigation system. Therefore, error correction technology has great significance for underwater inspection and operation tasks. This paper takes the SINS/DVL integrated positioning and navigation system as the research object and deeply studies the DVL error correction technology in the integrated system. [ABSTRACT FROM AUTHOR]

The large-acceleration motion of HFV (hypersonic flight vehicle), the high-speed rolling of spinning missile, and the large-maneuver flight of fighter aircraft has put forward higher performance demand for SINS (strapdown inertial navigation system). The high-accuracy positing will be realized under the high-dynamic maneuver environment after decreasing measurement error of IMU (inertial measurement unit), meanwhile the algorithm of SINS must be improved. The conventional algorithm calculates the flight attitude with determining the compensation term of coning error, after ignoring the high-order term of the Bortz equation. To improve the algorithm accuracy of SINS under high-dynamic maneuver environment, a high-accuracy algorithm, which uses Legendre polynomial to complete angular velocity function approximation and takes the numerical method of quaternion differential equation as core, is proposed herein. The high-order coning error is compensated in the numerical solving period in the proposed novel algorithm, because no approximation exists in deducing process. The attitude calculating simulations are finished in coning motion condition and high-dynamic maneuver condition respectively. Compared with the quadruple-cross-product compensation algorithm which has the highest accuracy at present, the attitude error of proposed algorithm is less than its 1/3 in coning motion condition. And algorithm accuracy is raised an order of magnitude under the high-dynamic maneuver environment. The high-accuracy algorithm based on Legendre polynomial has reference significance for accurate positing of future HFV, atomic gyroscope INS research and high-accuracy algorithm design of SINS.

Compared with the strapdown inertial navigation system (SINS), the rotation strapdown inertial navigation system (RSINS) can effectively improve the accuracy of navigation information, but rotational modulation also leads to an increase in the oscillation frequency of attitude errors. In this paper, a dual-inertial navigation scheme that combines the strapdown inertial navigation system and the dual-axis rotation inertial navigation system is proposed, which can effectively improve the attitude error accuracy in the horizontal direction by using the high-position information of the rotation inertial navigation system and the stability characteristics of the attitude error of the strapdown inertial navigation system. Firstly, the error characteristics of the strapdown inertial navigation system and the rotation strapdown inertial navigation system are analyzed, and then the combination scheme and Kalman filter are designed according to the error characteristics, and finally, the simulation experiment shows that the pitch angle error of the dual inertial navigation system is reduced by more than 35% and the roll angle error is reduced by more than 45% compared with the rotation strapdown inertial navigation system. Therefore, the combination scheme of double inertial navigation proposed in this paper can further reduce the attitude error of the rotation strapdown inertial navigation system, and at the same time, the two sets of inertial navigation systems can also enhance the reliability of ship navigation. [ABSTRACT FROM AUTHOR]

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the article considers the possibility of implementing an autonomous mode in the navigation complex of a small-sized tactical unmanned aerial vehicle, including a strapdown inertial navigation system and an onboard vision system. Due to the fact that the accuracy of such navigation systems significantly depends on changes in the background-target environment and equipment failures, it is proposed to organize automatic adaptation of the system to changes in external conditions and internal factors by controlling the operating modes and parameters of the software that implements optimal estimation algorithms. For this purpose, a model of an onboard inertial-optical autonomous navigation system has been developed in the class of discrete dynamic systems with a random change in structure.

This paper aims to assess the possibility of using functionally redundant inertial units to solve problems of increasing reliability and ensuring the fault tolerance of the various classes and purposes of aircraft navigation systems. We present the results of studying failure detection methods to improve the accuracy and reliability of a strapdown functionally redundant inertial unit. The resulting structural redundancy of the strapdown inertial measurement unit is designed to increase the fault tolerance and accuracy of strapdown inertial navigation systems. The methods for detecting sensor failures in functionally redundant inertial units are based on the use of the equations of functionally redundant inertial unit compliance to nominal requirements for the accuracy of measuring the input action vector. To describe the methods for detecting and eliminating failed sensor and algorithm designs based on them, we gave the mathematical models of the measurement vector of functionally redundant inertial units concerning the measured vector and the error identification condition, including the residual of the matching equations with the size due to the level of redundancy, determining the total number of matching equations. The main criterion for determining a failed sensor is non-compliance with the nominal value of the residual included in a certain number of matching equations of the information received from such meters. The developed algorithms are examined using simulation methods. The study of the selected structure of the functionally redundant inertial units shows that the proposed approaches are efficient. Also, we manage to identify the main characteristics of the algorithms for detecting sensor failures that are structurally a part of the functionally redundant inertial units.

The factors are investigated that allow you to improve the ability of error estimation for the strapdown inertial navigation system inertial sensors—gyroscopes and accelerometers—during the flight of the aircraft. The main tool that solves this problem is the trajectory conditions that ensure the alternation of longitudinal acceleration. Several scenarios of aircraft movement with variable longitudinal acceleration are considered. The results of simulation of inertial sensor error estimation are presented. [ABSTRACT FROM AUTHOR]

The guidance system of a fire-extinguishing bomb in the form of a hinged guidance kit is considered, which turns the bomb into a simple and fairly cheap fire-extinguishing device of the "throw and forget" type, optimized for mass production and use. In order to increase the accuracy of dropping a fire-extinguishing bomb, it is proposed to install a cheap high-precision and compact GPS navigation satellite system and a strapdown inertial system on an ordinary fire-extinguishing bomb. The algorithm of complex information processing of their systems, built on the basis of the improved method of the mutual compensation, in which for improve the quality of the error estimation procedure, the sluggishness of second-order Butterworth filters is compensated is proposed. The guidance system controls the trajectory of the fire-extinguishing bomb as a means of "first strike" and this increases the efficiency of further use of traditional flood systems by 50...70 times. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Indoor pedestrian positioning has been widely used in many scenarios, such as fire rescue and indoor path planning. Compared with other technologies, inertial measurement unit (IMU)-based indoor positioning requires no additional equipment and has a lower cost. However, IMU-based indoor positioning has the problem of error accumulation, resulting in inaccurate positioning. Therefore, this paper proposes a cascade filtering algorithm to correct the accumulated error using only a small amount of map information. In the lower filter, the zero-velocity correction and the attitude-extended complementary filtering (ECF) algorithm are utilized to initially solve the pedestrian's trajectory. In the upper filter, a particle filter (PF) combined with the map information is adopted to correct the accumulated error of the heading and stride length. In the 2D positioning process, the root mean square error (RMSE) of the proposed algorithm is only 1.35 m. In the altitude correction, this paper proposes a method of clustering floor discrimination to deal with the instability of the barometer resulting from an uneven pressure and temperature. In the final 3D positioning experiment, with a total length of 536.5 m and including the process of going up and down the stairs, the end-point error is only 2.45 m by the proposed algorithm. [ABSTRACT FROM AUTHOR]

Precise positioning of underwater vehicles using only a single beacon for range measurement is one of the toughest challenges for underwater navigation, especially when the underwater vehicle travels along a straight path resulting in the system unobservable. In this paper, acoustic range measurements are combined with inertial navigation to determine the position of an underwater vehicle using two consecutive measurement points. The contributions of the work presented here are twofold: First, the underwater single beacon localization algorithm proposed in this paper is able to operate under a linear track, and give the solution of the method to reject multi-valuedness. Second, considering the actual work of underwater carriers, extending the strategy to any trajectory can be used. The results obtained by the algorithm are fed back to the inertial navigation system, which can suppress the continued dispersion of inertial navigation errors. The experimental results show that the proposed “Linear track underwater carrier Strapdown Inertial Navigation System (SINS) correction method based on hydroacoustic single beacon” can solve the positioning problem of underwater long-range carriers and suppress further dispersion of their inertial navigation system errors.

To improve the reliability of strapdown inertial navigation system (SINS)/Doppler radar/odometer integrated navigation system, the federated Kalman filter with two-stage fault detection structure is designed, and a fault-tolerant SINS/Doppler radar/odometer integrated navigation method is proposed. Firstly, the pre-fault detection module sets before the local filter, and the residual chi-square test in the carrier coordinate system is selected to detect the abrupt faults of Doppler radar and odometer. Then, the secondary-fault detection module emplaces between the local filter and the main filter, and the sequential probability ratio test (SPRT) is selected to further detect the ramp faults that are difficult to detect by the residual chi-square test. To address the limitation of the SPRT in accurately determining the end time of faults, an improved SPRT is proposed. The improved SPRT reduces the influence of historical fault on the fault statistics by introducing forgetting factors to improve its sensitivity to the fault end. The simulation experiment indicates that the proposed method can quickly detect and isolate abrupt and ramp faults, and promptly restore normal operation of the integrated navigation system after the fault ends, effectively improving the fault tolerance and reliability of the integrated navigation system.

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Underwater vehicles are key carriers for underwater inspection and operation tasks, and the successful implementation of these tasks depends on the positioning and navigation equipment with corresponding accuracy. In practice, multiple positioning and navigation devices are often combined to integrate the advantages of each equipment. Currently, the most common method for integrated navigation is combination of the Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL). Various errors will occur when SINS and DVL are combined together, such as installation declination. In addition, DVL itself also has errors in the measurement of speed. These errors will affect the final accuracy of the combined positioning and navigation system. Therefore, error correction technology has great significance for underwater inspection and operation tasks. This paper takes the SINS/DVL integrated positioning and navigation system as the research object and deeply studies the DVL error correction technology in the integrated system.

In this paper, an improved in-motion coarse alignment method is proposed for a strapdown inertial navigation system (SINS) using position loci obtained from the Global Positioning System (GPS). The difference from the popular coarse alignment methods is that the proposed algorithm uses GPS position loci information to form the vector observation, and does not need velocity information, which expands the application range of in-motion coarse alignment. In addition, this paper utilizes the Optimal-REQUEST algorithm to reduce the influence of random errors contained in the vector observation. The Optimal-REQUEST algorithm is an adaptive iterative updating algorithm, which can adaptively adjust the gain of the filter according to the loss function. Simulation results confirmed that the proposed algorithm can suppress the impact of random errors effectively. The pitch, roll and yaw angles calculated by the proposed algorithm were improved by 51.95%, 53.80% and 63.03% compared with the comparison algorithms. [ABSTRACT FROM AUTHOR]

Doppler velocity log (DVL) errors affect the accuracy of strapdown inertial-navigation system (SINS)/DVL integrated navigation. Most existing calibration methods do not consider the problem of DVL sampling frequencies that are not fixed, and they cannot cope with large misalignment angles. This study proposes a method to calibrate internal and external DVL errors simultaneously, based on a genetic algorithm. To solve the problem of asynchronous SINS and DVL sampling, a method for unifying the time reference using cubic-spline interpolation is proposed. An error model for the DVL is established; the scale factor and misalignment angles are used as optimization genes for a genetic algorithm. The method can achieve high accuracy without requiring the misalignment angles to be small, significantly reducing the practical difficulty of installation. The results of simulation and shipboard experiments indicate that the calibration accuracy of the proposed algorithm is significantly better than that of the traditional method. For example, when the misalignment angle reaches 15°, the proposed algorithm has only a small error. If the carrier pitch angle changes to 5°, the root-mean-square error is reduced by 31.15% in the x-direction and 69.15% in the z-direction under highly dynamic conditions. The method thus seems likely to be useful in real-world engineering settings. [ABSTRACT FROM AUTHOR]

In order to combat the severity of the impact of short-term failure Doppler velocity log (DVL), we propose a machine learning (ML) aided method for strapdown inertial navigation system (SINS)/DVL integration solution. First, the inherent relationship between the underwater vehicle's dynamics characteristic and the SINS's velocity error is established through the learning methodology of the least square support vector machine (LS-SVM), and the prediction and compensation are performed during the failure period of the DVL. When the DVL signal is normal, the LS-SVM model is trained, the adaptive Kalman filtering (AKF) is adopted in the SINS/DVL integrated navigation system, the filtering estimation value is used to correct the SINS's navigation calculation value. When the DVL signal is invalid, the variation of underwater vehicle movement is taken as the input of the LS-SVM model. Land vehicle field experiment is conducted to verify the feasibility and effectiveness of the LS-SVM/AKF algorithm aided SINS/DVL integrated navigation system. The results indicate that the proposed methodology can improve the accuracy of the SINS/DVL integrated navigation system during short-term failure of DVL. [ABSTRACT FROM AUTHOR]

In underwater navigation systems, Global Navigation Satellite System (GNSS) information cannot be used for navigation. The mainstream method of autonomous underwater vehicles (AUV) underwater navigation system is Doppler Velocity Log (DVL) aided strapdown inertial navigation system (SINS). However, because the DVL is an instrument based on Doppler frequency shift to measure velocity, it is easily affected by the external environment. In a complex underwater environment, DVL output is easily polluted by outliers or even interrupted. In this paper, A new integrated navigation algorithm based on deep learning model is proposed to deal with DVL malfunctions. First, use RKF based on Mahalanobis distance algorithm to eliminate outliers, and then train the Nonlinear AutoRegressive with eXogenous input (NARX) model when DVL is available. When DVL is interrupted, use the NARX model to predict the output of DVL and continue integrated navigation. The proposed NARX-RKF scheme’s effectiveness verification was performed on the data set collected by the SINS/DVL ship-mounted experimental system. For comparison, different methods are also compared in the experiment. Experimental results show that NARX-RKF can effectively predict the output of DVL and is significantly better than other methods.

Copyright of Electronics & Control Systems is the property of National Aviation University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract—Precision agriculture makes use of high-accuracy navigation, attitude determination, and obstacle detection methods to save resources and to obtain better results. The collected robot position and attitude, and obstacle location data can be effectively employed to synthesize control algorithms for autonomous agricultural machines. These algorithms are applied for coverage path planning, route planning, motion stabilization along the specified paths, obstacle avoidance, and ensuring guaranteed behavior. These tasks are considered in the paper. [ABSTRACT FROM AUTHOR]

In order to solve the problems that roadheader positioning method based on total station could not locate because light path could be blocked due to excessive dust in coal mine underground and cumulative error of the method based on strapdown inertial navigation system(SINS) increased gradually with time, a roadheader positioning method combining total station and SINS was proposed. Firstly, total station is used to measure position parameters of roadheader, and SINS is used to measure and calculate position and posture parameters. Then, longitude and latitude of roadheader position measured by SINS are converted into coordinate values under Xi'an 80 coordinate system, so as to realize unification with total station measurement coordinate system. Finally, Kalman filtering method is used to fuse measured data of total station and SINS, so as to obtain position and attitude data of roadheader. The experimental results show that the method has high poisoning precision: in x direction, the maximum positioning error is 0.029 1 m, the minimum error is 0.010 0 m, and the average error is 0.019 93 m; in y direction, the maximum positioning error is 0.029 5 m, the minimum error is 0.011 0 m, and the average error is 0.018 26 m.

Compared with the strapdown inertial navigation system (SINS), the rotation strapdown inertial navigation system (RSINS) can effectively improve the accuracy of navigation information, but rotational modulation also leads to an increase in the oscillation frequency of attitude errors. In this paper, a dual-inertial navigation scheme that combines the strapdown inertial navigation system and the dual-axis rotation inertial navigation system is proposed, which can effectively improve the attitude error accuracy in the horizontal direction by using the high-position information of the rotation inertial navigation system and the stability characteristics of the attitude error of the strapdown inertial navigation system. Firstly, the error characteristics of the strapdown inertial navigation system and the rotation strapdown inertial navigation system are analyzed, and then the combination scheme and Kalman filter are designed according to the error characteristics, and finally, the simulation experiment shows that the pitch angle error of the dual inertial navigation system is reduced by more than 35% and the roll angle error is reduced by more than 45% compared with the rotation strapdown inertial navigation system. Therefore, the combination scheme of double inertial navigation proposed in this paper can further reduce the attitude error of the rotation strapdown inertial navigation system, and at the same time, the two sets of inertial navigation systems can also enhance the reliability of ship navigation.

The paper studies the possibility of updating the parameters of error model of a rotating inertial measurement unit (IMU) with fiber-optic gyroscopes (FOG) in a strapdown inertial navigation system (SINS) under operating conditions. The IMU is placed in a two-axis gimbal for modulation rotation. The main focus is made on the estimation of scale factor errors of the FOG and accelerometers, non-orthogonality of their sensitive axes, and relative time delays (group delays) of inertial sensors during the IMU normal rotation according to the navigation solution of the INS in the observation mode of its operation. Also, the paper presents the description and results of estimation of so-called rhumb drifts of the IMU, which may occur due to the perturbing forces associated with the geographical axes or the axes of the system central device body. The research is based on the results of FOG-based INS simulation. [ABSTRACT FROM AUTHOR]

Communication delay and non-Gaussian noise are challenging issues for underwater navigation and positioning. This study proposes a filtering algorithm for strapdown inertial navigation system/ultra-short baseline (SINS/USBL) integrated navigation to deal with time-varying delay in underwater acoustic communication and cope with non-Gaussian noise induced by outliers and measurement noises. Considering the influence of platform error angle, the measurement equation of SINS/USBL is derived. According to the distance-related time delay characteristics of USBL acoustic communication, the delay system model is obtained based on state inversion. A linear recursive model based on a delay system model is constructed to update the posterior estimation and covariance matrix by combining it with the maximum correntropy criterion. The algorithm solves the problems of communication delay and non-Gaussian noise and greatly reduces the computational complexity due to its adaptive adjustment function. Simulation and experimental results verify the filter's improved accuracy and robustness. [Display omitted] • A novel SINS/USBL integrated navigation model is proposed for underwater navigation. • A delay system model is derived to deal with the time-varying measurement delay. • Maximum correntropy criterion is introduced into a reconstructed linear recursive model. • Co-processing time delay and non-Gaussian noise is the key to improving accuracy. [ABSTRACT FROM AUTHOR]

Indoor pedestrian positioning has been widely used in many scenarios, such as fire rescue and indoor path planning. Compared with other technologies, inertial measurement unit (IMU)-based indoor positioning requires no additional equipment and has a lower cost. However, IMU-based indoor positioning has the problem of error accumulation, resulting in inaccurate positioning. Therefore, this paper proposes a cascade filtering algorithm to correct the accumulated error using only a small amount of map information. In the lower filter, the zero-velocity correction and the attitude-extended complementary filtering (ECF) algorithm are utilized to initially solve the pedestrian’s trajectory. In the upper filter, a particle filter (PF) combined with the map information is adopted to correct the accumulated error of the heading and stride length. In the 2D positioning process, the root mean square error (RMSE) of the proposed algorithm is only 1.35 m. In the altitude correction, this paper proposes a method of clustering floor discrimination to deal with the instability of the barometer resulting from an uneven pressure and temperature. In the final 3D positioning experiment, with a total length of 536.5 m and including the process of going up and down the stairs, the end-point error is only 2.45 m by the proposed algorithm.

To accurately track body attitude under high dynamic environments, a new attitude updating algorithm for the strapdown inertial navigation system is proposed after further applying higher degree polynomial to the quaternion Picard iteration (QPI) algorithm. With QPI, calculation error introduced by Picard approximation can be eliminated, but the angular rate fitting error introduced by substituting polynomial for angular rate of body will still affect the accuracy of the attitude updating algorithms which are designed based on polynomial model. Hence, a five- rather three-degree polynomial constructing method using four samples of gyro outputs with coning motion constrain is designed and tested. Simulation results indicate the proposed method owns more accuracy than QPI, optimal coning algorithm, and Fourth4Rot under both low and high dynamic environments. [ABSTRACT FROM AUTHOR]

Accurate and fast estimation of attitude is an important performance criteria for Strapdown Inertial Navigation System (SINS). Although many algorithms for SINS attitude estimation have been proposed by related researchers, most of them take the gravity vector as a known quantity. However, the gravity vector is inaccurate or even unknown in many cases, we have to use normal gravity instead of true gravity, which will bring errors to the mechanics equation of SINS, and reduce accuracy of attitude estimation. The coupling of gravity and attitude has been widely concerned by scholars and lack of effective solutions. In this paper, a new coarse attitude estimation method for dynamic base with decoupling the gravity vector is proposed. The method does not require the participation of gravity vector in attitude estimation process. Numerical simulation based on field test data is employed to verify the effectiveness and superiority of the proposed method in land vehicle navigation. The results indicate that the method improved the error caused by inaccurate gravity vector in traditional SINS attitude estimation and it provides a new idea for the measurement of vertical deviation. [ABSTRACT FROM AUTHOR]

Closed-form analytical representations of the rigid body orientation quaternion, angular velocity vector and the external moment vector satisfying kinematic equations and equations of motion are derived. In order to analyze errors of orientation algorithms for strapdown inertial navigation systems, reference models for specific rigid body rotation cases are formulated. Based on solutions, analytical expressions for ideal signals of angular velocity sensors in the form of quasi-coordinates are derived. For several sets of parameters, numerical implementations of the reference models are performed and trajectories in the configuration space of orientation parameters are presented. Numerical analysis of the drift error for the third-order orientation algorithm is performed. The results show that the value of the accumulated drift error using the derived two-frequency models exceeds the value of the accumulated drift error in the conventional case of a regular precession. [ABSTRACT FROM AUTHOR]

We examine two aspects specific to data fusion of strapdown inertial navigation systems (SINS) aided by global navigation satellite systems (GNSS), with their inherent spatial separation between the GNSS antenna phase center and the inertial measurement unit, as well as with SINS/GNSS synchronization errors. The first aspect refers to modifying the mathematical models used in INS/GNSS integration. The second one relates to our experience in their application in onboard airborne navigation algorithms developed by the Moscow Institute of Electromechanics and Automatics. [ABSTRACT FROM AUTHOR]