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The inertial measurement method of pipelines utilizes a Micro-Electro-Mechanical Systems Inertial Measurement Unit (MIMU) to get the three-dimensional trajectory of underground pipelines. The initial attitude is significant for the inertial measurement method of pipelines. The traditional method to obtain the initial attitude uses three-axis magnetometers to measure the Earth's magnetic field. However, the magnetic field in urban underground pipelines is intricate, which leads to the initial attitude being inaccurate. To overcome this challenge, a novel multi-position initial alignment method based on data backtracking for a single-axis FOG and a three-axis Micro-Electro-Mechanical Inertial Measurement Unit (MIMU) is proposed. Firstly, the configuration of the sensors is determined. Secondly, according to the three-point support structure of the pipeline measuring instrument, a three-position alignment scheme is designed. Additionally, an initial alignment algorithm using the data backtracking method is developed. In this algorithm, a rough initial alignment is conducted by the data from single-axis FOG, and a fine initial alignment is conducted by the data from FOG/MIMU. Finally, an experiment was conducted to validate this method. The experiment results indicate that the pitch and roll angle errors are less than 0.05°, and the azimuth angle errors are less than 0.2°. This improved the precision of the 3-D trajectory of underground pipelines. [ABSTRACT FROM AUTHOR]

Abstract This work introduces a novel approach to Strapdown Inertial Navigation System (SINS) alignment, distinct from recursive methods like Kalman filtering. The proposed methodology expedites bias error calculations by utilizing quaternion-based analytical relationships, which bypasses the slow convergence behavior associated with recursive algorithms, particularly in azimuth angle error estimation. In addition, the proposed approach demonstrates comparable accuracy to traditional fine alignment methods. Simulations and experiments validate that in contrast to the 10-min time requirement of traditional fine alignment methods (for azimuth angle estimation in stationary conditions), the proposed approach achieves the same accuracy within 20 s. However, limitations exist as the algorithm is applicable only in stationary conditions, and necessitating a high-grade IMU capable of measuring the earth’s rotation rate.

In the initial alignment of shaking base, Kalman filter is easy to diverge, and the alignment result is poor. Many scholars introduce various nonlinear filtering methods to solve this problem, but these filtering methods not only have large amount of calculation, but also are low accuracy. In theory, standard Kalman filtering can obtain the optimal estimate of the state under the condition that the structural parameters and noise statistical characteristic parameters of the random system are accurately known. Based on the problem, an improved adaptive Kalman filter (IAKF) algorithm is proposed to complete the initial alignment of the shaking base. While performing state estimation, it can also estimate the system's noise parameters in real time through measurement output. The experimental results show that the improved algorithm has high anti-interference and anti-divergence ability. Under the simulation conditions, pitch, roll and heading errors can be stabilized at about 10 degrees. The real measurement with Redmi K30Pro and iPhone 14 mobile phone shows that the alignment result of the improved algorithm is consistent, and the difference of multiple alignment results is stable within 5 degrees, while the difference of traditional method is more than 20 degrees. It can be applied in smart phone navigation with the strapdown inertial navigation system. [ABSTRACT FROM AUTHOR]

This article presents a method for enhancing the accuracy of the initial alignment process of inertial navigation systems with a stabilized platform using state feedback control in flight mode. In the proposed method, a state feedback controller is designed utilizing stable platform deviation angles and sensor errors extracted via a Kalman filter. By verifying the system's observability and incorporating suitable flight maneuvers, the navigation error propagation equations are expressed as a time-invariant system, enabling the estimation of sensor angles and errors during the alignment phase. This lays the groundwork for state feedback design. Subsequently, considering the stable platform's motion equations and applying the principle of separation of observer and controller design, a state feedback controller is developed. Finally, simulation results demonstrate that the proposed method improves the accuracy of the alignment process and, consequently, enhances navigation accuracy compared to the conventional output feedback method.

This paper proposes an improved initial alignment method for a strap-down inertial navigation system/global navigation satellite system (SINS/GNSS) integrated navigation system with large misalignment angles. Its methodology is based on the three-dimensional special Euclidean group and extended Kalman filter (SE2(3)/EKF) and aims to overcome the challenges of achieving fast alignment under large misalignment angles using traditional methods. To accurately characterize the state errors of attitude, velocity, and position, these elements are constructed as elements of a Lie group. The nonlinear error on the Lie group can then be well quantified. Additionally, a group vector mixed error model is developed, taking into account the zero bias errors of gyroscopes and accelerometers. Using this new error definition, a GNSS-assisted SINS dynamic initial alignment algorithm is derived, which is based on the invariance of velocity and position measurements. Simulation experiments demonstrate that the alignment method based on SE2(3)/EKF can achieve a higher accuracy in various scenarios with large misalignment angles, while the attitude error can be rapidly reduced to a lower level. [ABSTRACT FROM AUTHOR]

The inertial measurement method of pipelines utilizes a Micro-Electro-Mechanical Systems Inertial Measurement Unit (MIMU) to get the three-dimensional trajectory of underground pipelines. The initial attitude is significant for the inertial measurement method of pipelines. The traditional method to obtain the initial attitude uses three-axis magnetometers to measure the Earth’s magnetic field. However, the magnetic field in urban underground pipelines is intricate, which leads to the initial attitude being inaccurate. To overcome this challenge, a novel multi-position initial alignment method based on data backtracking for a single-axis FOG and a three-axis Micro-Electro-Mechanical Inertial Measurement Unit (MIMU) is proposed. Firstly, the configuration of the sensors is determined. Secondly, according to the three-point support structure of the pipeline measuring instrument, a three-position alignment scheme is designed. Additionally, an initial alignment algorithm using the data backtracking method is developed. In this algorithm, a rough initial alignment is conducted by the data from single-axis FOG, and a fine initial alignment is conducted by the data from FOG/MIMU. Finally, an experiment was conducted to validate this method. The experiment results indicate that the pitch and roll angle errors are less than 0.05°, and the azimuth angle errors are less than 0.2°. This improved the precision of the 3-D trajectory of underground pipelines.

The initial alignment of the real time kinematic(RTK) receiver onboard a low-cost micro-electro-mechanical system-inertial measurement unit (MEMS-IMU) is a key technical issue and a challenge for current inertial RTK. Aiming at this problem, an initial alignment method was proposed for inertial navigation RTK based on gradient descent, which combined multi-vector attitude determination with gradient descent, which effectively improved the accuracy of multi-vector attitude determination. The effectiveness of the improved algorithm was verified by simulation experiments and field experiments. The experimental results show that without using any additional sensors (such as magnetometers), using a low-cost MEMS-IMU, it takes only 2 to 3 s to tilt the centering rod within 40° with 91% confidence. The measurement accuracy of RTK reaches 2. 5 cm, and the accuracy and convergence speed of the algorithm can well meet the requirements of inertial navigation RTK applications. [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]

This paper proposes an improved initial alignment method for a strap-down inertial navigation system/global navigation satellite system (SINS/GNSS) integrated navigation system with large misalignment angles. Its methodology is based on the three-dimensional special Euclidean group and extended Kalman filter (SE2(3)/EKF) and aims to overcome the challenges of achieving fast alignment under large misalignment angles using traditional methods. To accurately characterize the state errors of attitude, velocity, and position, these elements are constructed as elements of a Lie group. The nonlinear error on the Lie group can then be well quantified. Additionally, a group vector mixed error model is developed, taking into account the zero bias errors of gyroscopes and accelerometers. Using this new error definition, a GNSS-assisted SINS dynamic initial alignment algorithm is derived, which is based on the invariance of velocity and position measurements. Simulation experiments demonstrate that the alignment method based on SE2(3)/EKF can achieve a higher accuracy in various scenarios with large misalignment angles, while the attitude error can be rapidly reduced to a lower level.

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) 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.)

Initial alignment technology is directly related to the navigation accuracy and startup time of the strap-down inertial navigation system (SINS), and has always been regarded as a challenging focal point in the research field of inertial navigation. This paper makes a comprehensive survey of SINS initial alignment technology, briefly introduces the basic principles of initial alignment without latitude, coarse alignment with known latitude and precise alignment, and points out their advantages, disadvantages and applicable conditions. The research and effects of existing initial alignment error suppression techniques are then analyzed and discussed. Finally, according to the problems of existing initial alignment methods and the development requirements of the carrier, the future research direction of SINS initial alignment technology is predicted.

In the polar region, the gravity vector and Earth's rotation vector tend to be in the same direction, leading to a slower convergence speed and longer alignment time of the moving base alignment. When the alignment time is short, the alignment cannot converge, resulting in low azimuth accuracy. To address this issue, we propose a polar moving base alignment method based on a backtracking scheme. Notably, this work first derives a polar coarse alignment method with the inertial frame based on the transverse Earth model. On this basis, we designed a polar coarse alignment method based on a backtracking scheme and optimized the data storage scheme. Then, a backward navigation algorithm based on the transverse inertial navigation mechanical arrangement scheme was derived, and a polar fine alignment method based on a backtracking scheme was designed. Semi-physical simulation experiments showed that the alignment algorithm based on a backtracking scheme could converge in the 180 s with high alignment accuracy, which is 70% faster than the current polar moving base alignment method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology 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 strapdown airborne gravimetry, initial alignment of a strapdown inertial measurement unit (IMU) of the gravimeter is of great importance. It is performed to determine the IMU attitude angles by the measurements during the aircraft standstill before the flight. This information, in its turn, helps determine the bias of the gravimeter vertical accelerometer. After the flight the bias is determined again and the linear drift over the entire flight can be removed from the vertical accelerometer measurements. However, the measurements of inertial sensors are disturbed by uncontrolled angular motions of the IMU due to external mechanical actions on the gravimeter body during the aircraft standstill. The paper proposes an algorithm for the gravimeter IMU initial and final alignments with the determination of IMU attitude angles and biases of all three accelerometers during the standstills before and after the flight. It has been shown in the paper that the accelerometer scale factor errors can also be determined by the algorithm. The algorithm performance under the angular motions of the gravimeter has been demonstrated on experimental data. The accelerometers calibration accuracy has been evaluated by comparing with the results of another calibration algorithm and by analyzing the errors in IMU autonomous dead-reckoning. [ABSTRACT FROM AUTHOR]

In this paper, a novel fast fine alignment method is proposed on the basis of micro electro mechanical systems (MEMS) for unmanned aerial vehicle (UAV) under mooring conditions. Firstly, the model of single-axis rotation modulation with extended angular rate measurement is built to improve the speed of convergence, especially the azimuth misalignment angle and the constant error of gyroscope along the rotation axis. Both model mismatch and noise uncertainty of the extended measurement will arise for the difficulty to obtain the exact angular velocity of the carrier without auxiliary sensors. To address this problem, the adaptive Kalman filter based on multiple fading factors is proposed. Besides, the meta-heuristic beetle antennae search (BAS) algorithm is applied for the first time to optimize the fading factors in real time. Then, the effectiveness of the proposed method is verified by means of singular value decomposition and error covariance matrix analysis. Finally, the result of comparative simulations and experiments show that the observability degree and convergence speed of misalignment angles improve significantly compared to those conventional methods and the estimated values can satisfy the requirements of fine alignment. More importantly, the constant error of gyroscope along the rotation axis is estimated accurately and can be compensated in the subsequent navigation process. [ABSTRACT FROM AUTHOR]

The initial alignment of the inertial navigation system of unmanned offshore platforms is interfered with the swaying motion induced by wave action. Departing from the traditional alignment model based on gravitational apparent motion, this paper derives the models of gravitational apparent acceleration and velocity in order to overcome the theoretical insufficiency of directly employing accelerometers to approximate gravity. Parameter identification and reconstruction of gravitational apparent motion are performed by recursive least squares algorithm (RLS) to determine the attitude of the strapdown inertial navigation system (SINS). Concurrently, a comparison is made between the calculated and reconstructed gravitational apparent motion for the separation of the platform's linear acceleration or velocity. Simulation and mooring experiment indicate that the method studied in this paper demonstrates superior convergence and accuracy compared to traditional anti-rocking coarse alignment methods. In particular, the parameter identification alignment method based on gravitational apparent velocity is capable of achieving the extreme accuracy of the inertial measurement unit (IMU). The error in separated linear motion does not diverge over time, which meets engineering requirements. This method has important implications for application scenarios such as the initialization of inertial navigation systems on unmanned platforms. • The proposed method significantly improves the accuracy and convergence of attitude angles compared with the traditional method. • The method addresses the theoretical limitations for alignment and allows the separation of linear acceleration or velocity. [ABSTRACT FROM AUTHOR]

To solve the problem of high-precision and fast initial alignment for the Strapdown Inertial Navigation System (SINS) under both dynamic and static conditions, the high-precision attitude measured by the celestial navigation system (CNS) is used as the reference information for the initial alignment. The alignment algorithm is derived in the Earth-centered inertial (ECI) frame. Compared with the alignment algorithm in the navigation frame, it is independent of position parameters and avoids the influence of the approximate error caused by the dynamic deflection angle. In addition, hull deformation is considered in attitude optimal estimation, which can realize initial the alignment of the SINS installed in various parts of the carrier. On this basis, the velocity measurement information is added to the alignment process, which further improves the accuracy and speed of the initial alignment under static conditions. The experimental results show that the algorithms proposed in this paper have better performance in alignment accuracy, speed, and stability. The attitude and velocity matching algorithm in the ECI frame can achieve alignment accuracy better than 0.6′. The attitude matching algorithm in the ECI frame has better robustness and can be used for both dynamic and static conditions, which can achieve alignment accuracy better than 1.3′. [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.)

This article deals with error models for a trident quaternion framework proposed in the companion paper (Part I) and further uses them to investigate the odometer-aided static/in-motion inertial navigation attitude alignment for land vehicles. By linearizing the trident quaternion kinematic equation, the left- and right-trident-quaternion error models are obtained, which are found to be equivalent to those derived from profound group affine. The two error models are used to design their corresponding extended Kalman filters (EKFs), namely, the left-quaternion EKF (LQEKF) and the right-quaternion EKF (RQEKF). Simulations and field tests are conducted to evaluate their actual performances. Owing to the high estimation consistency, the LQEKF and the RQEKF converge much faster in the static alignment than the traditional error-model-based EKF, even under arbitrary large heading initialization. For the in-motion alignment, the LQEKF and the RQEKF possess much larger convergence region than the traditional EKF does, although they still require the aid of attitude initialization so as to avoid large initial attitude errors. [ABSTRACT FROM AUTHOR]

Initial alignment is of vital importance to the inertial navigation system, and the in-motion initial alignment is a critical stage for the moving vehicles. In this paper, an in-motion alignment method for SINS/GPS integration is investigated. Using the velocity, which is obtained from GPS, the observation and the reference vectors are constructed. Different from previous methods, a robust attitude determination method is devised to finish the in-motion alignment process when there are outliers contained in the GPS outputs. To implement the robust attitude determination method, the error of the magnitudes of the observation and reference vectors is calculated, and this error is used to detect the outliers. However, the bias of the inertial sensors, the expectation and variance of the calculated error of the magnitude is time-varying, so that the detected accuracy of the outliers degrades. To address this defect, the new normalized error of the magnitude is constructed by a robust parameter identification method, then the expectation and variance of the new normalized error are constant. Using the new normalized error, the initial attitude is obtained by the robust optimization-based alignment (ROBA) method. The simulation and field tests are designed to validate the performance of the proposed method, the alignment results show that the proposed method can detect the outliers accurately, and it gets the similar alignment accuracy with the current popular method when there are no outliers in the auxiliary velocity of GPS. [ABSTRACT FROM AUTHOR]

The task of strapdown inertial navigation system (SINS) initial alignment is to calculate the attitude transformation matrix from body frame to navigation frame. In this paper, such attitude transformation matrix is divided into two parts through introducing the initial inertially fixed navigation frame as inertial frame. The attitude changes of the navigation frame corresponding to the defined inertial frame can be exactly calculated with known velocity and position provided by GNSS. The attitude from body frame to the defined inertial frame is estimated based on the SINS mechanization in inertial frame. The attitude, velocity and position in inertial frame are formulated together as element of the group of double direct spatial isometries (). It is proven that the group state model in inertial frame satisfies a particular “group affine” property and the corresponding error model satisfies a “log-linear” autonomous differential equation on the Lie algebra. Based on such striking property, the attitude from body frame to the defined inertial frame can be estimated based on the linear error model with even extreme large misalignments. Two different error state vectors are extracted based on right and left matrix multiplications and the detailed linear state-space models are derived based on the right and left errors for SINS mechanization in inertial frame. With the derived linear state-space models, the explicit initial alignment procedures have been presented. Extensive simulation and field tests indicate that the initial alignment based on the left error model can perform quite well within a wide range of initial attitude errors, although the used filter is still a type of linear Kalman filter. This method is promising in practical products abandoning the traditional coarse alignment stage. [ABSTRACT FROM AUTHOR]

In the future, a pedestrian navigation system (PNS) will be widely applied. In navigation system, an initial alignment is essential before starting navigation. There are two primary alignment methods, one is an inertial/magnetometer assisted alignment method, the other is a multiposition rotation modulation alignment method. However, magnetometer is susceptible to magnetic disturbance, and the large volume of rotary modulation system is not suitable for PNS. To solve these problems, a compound navigation system (CPNS) is proposed. It consists of a single-axis fiber-optic gyro (FOG) and a triaxial microelectromechanical system inertial measurement unit (MIMU) without modulation mechanism. CPNS is placed in different positions. According to the variation law of cosine function, FOG data in multiposition are used as fitting alignment, MEMS gyro tracks attitude changes between different positions. The experimental results demonstrate that initial alignment accuracy can be well calculated with several multiposition schemes under different conditions. This method could be a novel solution for PNS's initial alignment.

To address the accuracy requirements of initial alignment of high-precision inertial navigation systems (INSs), gravity disturbance compensation for INSs based on a spherical harmonic model is investigated herein. First, the horizontal component of gravity disturbance at an alignment point is calculated using the high-resolution Earth Gravity Model EIGEN-6C4 and then compensated to the initial alignment. Subsequently, the self-alignment algorithm of solidified coordinate frame is used to derive the misalignment angle equation of gravity disturbance affecting the initial alignment. Meanwhile, the coupling relationship between the measurement error of an inertial unit and the gravity disturbance is simulated and analyzed. Finally, a laser strapdown inertial navigation system experiment is performed. The simulation result shows that the pitch angle, roll angle, and heading angle errors reduced by 27.41", -0.37", and 6.72", respectively, after the gravity disturbance compensation. Experiment result shows that the alignment performance after compensation has been improved and the heading angle error is reduced by 6.76". The simulations and experiments results validate the theoretical analysis.

Rotation modulation can significantly improve the navigation accuracies of an inertial navigation system (INS) and a strap-down configuration dominating in this type of INS. However, this style of construction is not a good scheme since it has no servo loop to counteract a vehicle manoeuvre. This paper proposes a rotary upgrading method for a rotational INS based on an inertially stabilized platform. The servo control loop is reconstructed on a four-gimbal platform, and it has the functions of providing both a level stability relative to the navigation frame and an azimuth rotation at a speed of 1:2◦/s. With the platform’s rotation, the observability and the convergence speed of the estimation for the initial alignment can be improved, as well as the biases of the gyroscopes and accelerometers be modulated into zero-mean periodic values. An open-loop initial alignment method is designed, and its detailed algorithms are delivered. The experiment result shows that the newly designed rotational INS has reached an accuracy of 0.38 n mile/h (CEP, circular error probable). The feasibility and engineering applicability of the designed scheme have been validated.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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.)

The article is devoted to deriving reference models for the problem of initial alignment of a strapdown inertial navigation system (INS) on a swing base. It is assumed that the system does not move relative to the Earth, but its body can make uncontrolled angular motions. The described models are based on the approximation of the readings of INS accelerometers from projections on the axes of the instrument reference frame "frozen" in the inertial space, and the orientation of the frame is determined by its position at the start of the alignment. [ABSTRACT FROM AUTHOR]

In this article, a novel robust initial alignment method for the strapdown inertial navigation system (SINS) and global positioning system (GPS) integrated system is proposed to eliminate the outliers containing in the outputs of GPS. There are two innovations in the proposed method. First, the traditional quaternion Kalman filter method is often used to estimate the constant attitude quaternion in the swaying base. However, this article devises the new quaternion state model, which can be used in the in-motion base, expanding the scope of application. Second, based on the Huber’s robust theory and the in-motion quaternion Kalman filter state model, the novel algorithm called the robust quaternion Kalman filter method is devised. This method can construct the improved observation vectors to eliminate the outliers and estimate the constant attitude quaternion more accurately than the traditional in-motion method when GPS contains outliers. The experiment using the miniature inertial measurement unit (MIMU)-based SINS aided by GPS has shown the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The in-motion alignment of the strapdown inertial navigation system (SINS) is still a challenging problem due to the complex environmental changes that occur during carrier movement. This article proposes a novel initial alignment method by integrating the advantages of the Special Orthogonal Group of order 3 [ $SO\left ({{3} }\right)$ ] representation and the optimal attitude estimation alignment idea. In this method, the ${SO}\left ({{3} }\right)$ is used to represent the initial attitude matrix, and the optimal attitude estimation alignment model is established by analyzing the influence of the sensor error during the calculation process. Next, state-dependent extended Kalman filter is investigated by mapping the $SO\left ({{3} }\right)$ equations to the Lie algebra space, and the Lagrange formula is used to separate the state-dependent errors in the Lie algebraic filtering equation. The simulation and experimental results indicate that the proposed initial alignment method can achieve better alignment accuracy and shorter alignment time than existing methods and can fulfill the in-motion alignment tasks for SINSs in various environments. [ABSTRACT FROM AUTHOR]

Micro-electro-mechanical system (MEMS)-based inertial measurement unit (IMU) together with single-frequency Global Navigation Satellite System (GNSS) receiver provides a cost-efficient solution for outdoor navigation. An accurate initial alignment is necessary to the GNSS/IMU integrated navigation system to guarantee a good performance. Existing in-motion alignment methods mostly rely on the velocity derived from the GNSS receiver, which is hard to acquire when the receiver suffers from temporary signal blockage or deliberate interference. To mitigate the problem, we propose a carrier Doppler-based initial alignment method, which extends the availability of attitude estimation to cases where only three satellites are available. The proposed method is composed of two steps: the first step estimates a coarse initial attitude without any prior knowledge, and the second step dynamically refines the attitude estimation. Field experiment results show that the proposed method can provide attitude estimation with enough accuracy when only three satellites are available and has equivalent performance compared with the traditional optimization-based-alignment (OBA) method under general cases. The implementation of the proposed method is available on https://github.com/Wiggrim/D2R. [ABSTRACT FROM AUTHOR]

In the initial alignment, the accuracy of static base alignment is mainly limited by the short alignment time, the poor observability of azimuth misalignment, and sensor error. Traditional alignment methods cannot take into account the alignment time and accuracy without relying on external measurement assistance. In order to solve these problems, a new alignment method for RINS (Rotary Inertial Navigation System) based on BKF (Backtracking Kalman filtering) is proposed in this article. Firstly, innovative rotation modulation technology is introduced into the initial alignment process to eliminate the influence of sensors bias and improve the observability of azimuth misalignment. Secondly, the first forward filtering constructs KF for optimal estimation while storing the data from RINS into computer. Finally, the result of the last optimal estimation is used as the initial, and proposed scheme performs multiple forward filtering and backtracking filtering processes. Then, an accurate attitude will be obtained quickly. The alignment accuracy of the proposed scheme under large misalignment angle was evaluated through simulation test and shearer alignment experiment. Comparing with other effective static base alignment schemes, the accuracy of azimuth misalignment angle in the proposed scheme was improved at least 65%, and alignment duration of proposed scheme reduced to 85% of these schemes. The experiment has proven that proposed scheme is a high precision self-alignment method suitable for underground shearer, which can obtain accurate initial attitude under short time and large misalignment angle conditions without the assistance from external measurement. [ABSTRACT FROM AUTHOR]

In order to solve problems of difficult positioning and low precision in positioning method of underground mobile robots, a positioning method of underground robot based on strapdown inertial navigation and odometer was proposed. The method uses Kalman filter to perform initial alignment for strapdown inertial navigation to determine initial coordinates of the positioning, and obtain initial attitude transformation matrix. Then, it uses strapdown inertial navigation to calculate position of the robot independently, and uses the speed information output by the odometer and the real-time attitude transformation matrix output by strapdown inertial navigation to calculate the dead reckoning, so as to obtain the position information of the robot again. In order to reduce influence of the accumulated error on the strapdown inertial navigation, it adopts odometer and strapdown inertial navigation to construct dead reckoning system, and uses Sage-Husa adaptive filtering to design combined positioning algorithm to select errors as the system state. After filtering calculation and correction, the accurate position information of the robot is obtained. The experimental results show that the method can realize real-time positioning of the robot and can effectively reduce influence of the accumulated errors of strapdown inertial navigation. And the method has higher positioning accuracy. After the robot moves 4.3 m in Y direction and 0.25 m in Z direction, the positioning error in Y direction is 0.25 m and the positioning error in Z direction is 0.005 m.

The strap-down inertial navigation system (SINS) is a commonly used sensor for autonomous underground navigation that can be used for shearer positioning under a coal mine. To overcome the performance degradation of the existing initial alignment algorithm based on an inertial frame with strong vibration conditions in underground coal mines, a novel initial alignment compensation strategy based on the dynamic model of the shearer is presented. This strategy mainly compensates for the linear vibration caused by the harsh environment based on the dynamic model of the shearer. Meanwhile, this strategy can effectively estimate the linear velocity of the shearer's fuselage and avoid the error caused by the system disturbance assumption. According to the shearer dynamics model, the linear velocity spectrum of the shearer's fuselage was determined. Then, based on the vibration characteristics, the initial alignment compensation model of the SINS was derived and the initial attitude angle was obtained. The simulation and experimental results show that the proposed compensation strategy can effectively improve the convergence speed and accuracy of the initial alignment and lay a foundation for the unmanned work of the shearer.

In this paper, a fast-initial alignment method with angular rate aiding based on robust Kalman filter is proposed. First, the traditional system model of the initial alignment is derived, and the angular rate aiding method in the navigation frame is studied. To address the defects of the traditional angular rate aiding alignment method, the angular rate aiding method in body frame is derived. Then, a state augmentation method is employed to address the correlation between process noise and the measurement noise, which exists in the angular rate aiding methods. Considering the practical application, it is hard to keep completely still when the initial alignment is carried out on the vehicle. Therefore, a Huber's M-estimation has been adopted to eliminate the external interferences, and the robustness of the proposed method has been improved. Then, an analytical method for the observability of the proposed method is studied. The reason why the proposed method is faster than the traditional method is analyzed in detail. Finally, simulated and field tests are designed to validate the performance of the proposed method. The results show that the proposed method can finish the initial alignment when it is carried on the vehicle.

This paper presents an innovative complete observable initial alignment method for strapdown inertial navigation system. This method negates the need for multi-position rotation or a complex rotating mechanism. First, the coupling relationship between the error state variables is analytically derived. Based on the equivalence relationship between the acceleration output and the angular rate output and error state variables, an improved extended-measurement equation is then established. The feasibility of the scheme that accelerates the convergence speed is theoretically demonstrated, and the observability of the proposed method is qualitatively and quantitatively analyzed in comparisons with established methods (piece-wise constant system, singular value decomposition, and error covariance matrix analysis). The results confirm that the improved measurement model enhances the observability of state variables to different extent and can achieve complete observability of the system. The proposed method not only can provide the fast and accurate estimation of the alignment error, but also can predict the gyro bias online. More specifically, the equivalent horizontal accelerometer output certainly accelerated the convergence of the horizontal alignment error, and introducing the equivalent gyroscope output improved the convergence speed of the heading alignment error. It is a potential candidate for speeding up the convergence of the stationary initial alignment.

Accelerometer bias in an inertial navigation system (INS) is the key factor determining the navigation accuracy. Two methods, named separated calibration and system calibration are always used for acceleration bias calibration. The former one needs a high precision turntable to provide reference and the latter cannot estimate accelerometer bias along horizontal direction because of coupling effects between horizontal misalignment angles and horizontal accelerometer bias. In this paper, a new estimation method is designed to estimate accelerometer biases and acquire high horizontal alignment accuracy without a turntable. In the proposed method, different expressions about gravity and accelerometer biases in an inertial frame are analyzed and coupling models about gravity and biases are constructed. Then, measurements from gyroscopes and accelerometers are used to construct gravitational apparent acceleration and Kalman filter used to estimate parameters describing apparent acceleration and estimate accelerometer bias. The simulation and turntable tests show that when the carrier is without translational but with swinging motion, the proposed method can finish gravitational apparent acceleration identification and accelerometer bias estimation effectively at the same time, and when the reconstructed gravitational apparent acceleration used for alignment, horizontal alignment errors can nearly approach zeros.

To solve the problem of high-precision and fast initial alignment for the Strapdown Inertial Navigation System (SINS) under both dynamic and static conditions, the high-precision attitude measured by the celestial navigation system (CNS) is used as the reference information for the initial alignment. The alignment algorithm is derived in the Earth-centered inertial (ECI) frame. Compared with the alignment algorithm in the navigation frame, it is independent of position parameters and avoids the influence of the approximate error caused by the dynamic deflection angle. In addition, hull deformation is considered in attitude optimal estimation, which can realize initial the alignment of the SINS installed in various parts of the carrier. On this basis, the velocity measurement information is added to the alignment process, which further improves the accuracy and speed of the initial alignment under static conditions. The experimental results show that the algorithms proposed in this paper have better performance in alignment accuracy, speed, and stability. The attitude and velocity matching algorithm in the ECI frame can achieve alignment accuracy better than 0.6′. The attitude matching algorithm in the ECI frame has better robustness and can be used for both dynamic and static conditions, which can achieve alignment accuracy better than 1.3′.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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 aircrafts equipped with BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated navigation system, BeiDou Navigation Satellite System information can be used to achieve autonomous alignment. However, due to the complex polar environment and multipath effect, BeiDou Navigation Satellite System measurement noise often exhibits a non-Gaussian distribution that will severely degrade the estimation accuracy of standard Kalman filter. To address this problem, a new polar alignment algorithm based on the Huber estimation filter is proposed in this article. Considering the special geographical conditions in the polar regions, the dynamic model and the measurement model of BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated alignment system in the grid frame are derived in this article. The BeiDou Navigation Satellite System measurement noise characteristics in the polar regions are analyzed and heavy-tailed characteristics are simulated, respectively. Since the estimation accuracy of standard Kalman filter can be severely degraded under non-Gaussian noise, a Kalman filter based on the Huber estimation is designed combining grid navigation system and generalized maximum likelihood estimation. The simulation and experiment results demonstrate that the proposed algorithm has better robustness under non-Gaussian noise, and it is effective in the polar regions. By employing the proposed algorithm, the rapidity and accuracy of the alignment process can be improved. [ABSTRACT FROM AUTHOR]

This study has presented an efficient adaptive unscented Kalman filter (AUKF) with the new measurement model for the strapdown inertial navigation system (SINS) to improve the initial alignment under the marine mooring conditions. Conventional methods of the accurate alignment in the ship's SINS usually fail to succeed within an acceptable period of time due to the components of external perturbations caused by the movement of sea waves and wind waves. To speed up convergence, AUKF takes into account the impact of the dynamic acceleration on the filter and its gain adaptively tuned by considering the dynamic scale sensed by accelerometers. This approach considerably improved the corrections of the current residual error on the SINS and decreased the influence due to the external perturbations caused by the ship's movement. Initial alignment algorithm based on AUKF is designed for large misalignment angles and verified by experimental data. The experimental test results show that the proposed algorithm enhanced the convergence speed of SINS initial alignment compared with some state-of-the-art existing approaches. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology 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.)

This study focuses on fast and robust outlier matches removal strategy to improve the efficiency and precision of initial alignment and further the quality of pairwise registration. Starts from the point matches obtained via feature detecting and matching, the distance disparity matrix derived from Euclidean invariants of rigid transformation is introduced, based on which a fast and effective pruning method is proposed to eliminate the outlier correspondences, especially the sharp ones. Then, the remaining matches are sent into the enhanced least‐square backward method to estimate an initial transformation in lesser attempts. Since most of the outliers are rejected, presented backward method could provide a finer alignment to input point clouds in higher efficiency than existing methods, and the following refining procedure converges to a more precise registration consuming fewer iterations, which have been proved in designed experiments. The thresholds employed in the pipeline are all automatically determined according to the actual resolution of input point clouds. Users are just required to control the error precision through a scale factor, in which way the inaccuracy and inconvenience of manually threshold defining are avoided.

In the global navigation satellite system denial environment, strap-down inertial navigation system (SINS) has to rely on the body-frame velocity output by autonomous velocity measurement equipment, such as odometer and Doppler velocity log, to implement the in-motion initial alignment. Considering the external velocity noise and the approximation existing in the body-frame velocity aided initial alignment model, the autonomous in-motion alignment for SINS with high precision is a difficult problem. Besides, the position updating of SINS cannot be realized only depending on the body-frame velocity. The position error will negatively impact the precision of the following fine alignment and navigation. In this paper, a backtracking velocity denoising-based autonomous in-motion initial alignment is proposed. Forward compass alignment and backward compass alignment are carried out, respectively, to denoise the external velocity. Body-frame velocity-based and navigation-frame velocity-based attitude determination are implemented, respectively, to gradually realize the attitude alignment. The contributions of the work presented here are twofold. First, vehicle velocity during the whole initial alignment process is accurately denoised and determined based on the backtracking compass alignment. Second, high precision position alignment is achieved during the attitude determination. The validity of the proposed method is verified based on field test data.

In order to improve the computational efficiency and alignment accuracy of a microelectromechanical system (MEMS)-based strap-down inertial navigation system (SINS), this article proposes a high-accuracy global positioning system (GPS)-aided coarse alignment method. The attitude matrix between current and initial body frames and the unknown gyro bias, accelerometer bias, and lever arm are jointly estimated based on the proposed closed-loop approach, where the attitude error and unknown parameters are jointly inferred based on the derived linear state-space model using the Kalman filter. Simulation and experimental results illustrate that the proposed GPS-aided coarse alignment method can achieve better accuracy than existing state-of-the-art coarse alignment methods for MEMS-based SINS. [ABSTRACT FROM AUTHOR]

Inertial frame alignment (IFA) for strapdown inertial navigation systems is widely considered to be a promising method due to its robustness to disturbances and noise. By using the velocity data from a Doppler velocity log (DVL), the IFA can be performed while the vehicle is in motion. However, this article reveals that the initial velocity error of the DVL at the startup continuously and considerably affects the precision of the in-motion IFA. Based on the formula derived with other sensor errors isolated, the azimuth error is proportional to the initial velocity error and is inversely proportional to the square of time, e.g., a 0.3 m/s measurement error in the initial velocity from the Doppler velocity log leading to a considerable azimuth error of more than 1.78° in five minutes. Moreover, the azimuth error becomes bulky if the latitude is high. The modified cost function with a constant vector is used to replace the cost function from the well-known Wahba’s problem. Thus, an alignment result with a much higher precision and with the initial velocity error suppressed can be obtained in a short period of time. The vehicular experimental results further verify the error analysis and the improved method. [ABSTRACT FROM AUTHOR]

A new robust quaternion Kalman filter is developed for accurate alignment of stationary strapdown inertial navigation system. Most fine alignment algorithms have tried to estimate the biases of gyroscopes and accelerometers to reduce the errors of the alignment process. In stationary platforms, due to fixed inputs for sensors, the summation of various errors such as fixed bias, misalignment, scale factor, and nonlinear errors acts like one bias error, and then the identification of each error will be impossible. The observability of gyros and accelerometers' biases has also been studied. But, nowadays, we know that all of these unknown parameters are not observable. Then this problem can increase the complication of the alignment algorithm. The accelerometers' errors mainly affect the errors of the roll and pitch angles, but a big portion of the heading's error results from the gyroscopes' errors. Modeling of all errors as additional states without considering the observability parameters has no benefits, but will increase the filter's dimension, so the filter's performance will decrease. In this study, due to the observability problem, a new robust multiplicative quaternion Kalman filter is designed for the alignment of a stationary platform. The presented algorithm does not estimate the sensors' errors, but it is robust to uncertainty in the sensors' errors. In the proposed scheme, the bounds of parameters' errors are introduced to filter, and the filter tries to remain robust with respect to these uncertainties. The method uses the benefits of quaternions in attitude modeling, and then the robust filter is adapted to work with quaternions. The ability of the new algorithm is evaluated with MATLAB simulations. The outcomes show that the presented algorithm is more accurate than other traditional methods. The extended Kalman filter with accelerometers' outputs and the horizontal velocities as the measurement equations and additive quaternion Kalman filter are used for comparisons. [ABSTRACT FROM AUTHOR]