Strapdown Inertial Navigation System Research Articles

Correction of a Strapdown Inertial Navigation System During Descent in the Atmosphere

The article deals with the problem on determining the angular position during descent on an apparatus with low lift-drag ratio. A solution is presented using the least squares method, which reduces to determining the orientation quaternion using a system of linear algebraic equations. The proposed method is based on the algebraic properties of quaternions. A vibrating string accelerometer and a fiber optic gyroscope are used to obtain acceleration and angular velocity measurements. Data on the speed and coordinates of the device are available according to the readings of the satellite navigation equipment.

Guided Trajectory Filtering for Challenging Long-Range AUV Navigation

Aiming at the challenging underwater navigation of autonomous underwater vehicle (AUV), this paper develops a novel filtering method with guided trajectory. The relatively low frequency of ultra-short baseline positioning system (USBL) measurement and the noise caused by complex scenes have a significant impact on the accuracy of AUV motion estimation. Although the existing adaptive extended Kalman filter (EKF) and adaptive unscented Kalman filter (UKF) which can roughly eliminate these outliers, the filtering results of these algorithms are not smooth and have the serious degree of the jitter, and then greatly affect the local consistency and quality of topographic map seamless stitching. We exploit the relatively smooth strap-down inertial navigation system (SINS) as the guided trajectory for motion estimation, and propose a trajectory smoothness preserving filter algorithm. This system has the advantage of maintaining local gradient consistency, which not only improves the navigation accuracy, but also ensures the smoothness of the estimation system. Simulation results show that our approach has high robustness and good smoothness. The effectiveness and practicability of the method are demonstrated by real experiments in multiple deep-sea areas with an average depth of more than 1000 meters in the southwest Indian Ocean.

A Rapid Self-Alignment Strategy for a Launch Vehicle on an Offshore Launching Platform

To reduce the impact of offshore launching platform motion and swaying on the self-alignment accuracy of a launch vehicle, a rapid self-alignment strategy, which involves an optimal combination of anti-swaying coarse alignment (ASCA), backtracking navigation, and reverse Kalman filtering is proposed. During the entire alignment process, the data provided by the strapdown inertial navigation system (SINS) are stored and then applied to forward and backtrack self-alignment. This work elaborates the basic principles of coarse alignment and then analyzes the influence of ASCA time on alignment accuracy. An error model was built for the reverse fine alignment system. The coarse alignment was carried out based on the above work, then the state of the alignment system was retraced using the reverse inertial navigation solution and reverse Kalman filtering with the proposed strategy. A cycle-index control function was designed to approximate strict backtracking navigation. Finally, the attitude error was compensated for after the completion of the first and the last forward navigation. To demonstrate the effectiveness of the proposed strategy, numerical simulations were carried out in a scenario of launch vehicle motion and swaying. The proposed strategy can maximize the utilization of SINS data and hence improve the alignment accuracy and further reduce the alignment time. The results show that the fully autonomous alignment technology of the SINS can replace the complex optical aiming system and realize the determination of the initial attitude of a launch vehicle before launch.

Algorithm of the autonomous initial alignment of sins with sequential filtering

An algorithm for an autonomous initial alignment of a strapdown inertial navigation system on a fixed or limitedly movable base is proposed and considered. It sequentially uses two Kalman averaging filters in. The first one determines one column of the directing cosines matrix (DCM) between the axes of the associated with the object and the geographical accompanying bases by the signals of the accelerometers. The second filter determines the second column of the matrix based on the gyroscope signals and the obtained estimates of the direction cosines. The third column is determined from known ratios. From the resulting DCM, the heading, pitch and roll angles are determined. Information signals for solving the problem are obtained by averaging a small interval (5 s) at the end of the Kalman filter transition process. This allows minimizing errors from changing the position of the object during the measurement time, which can reach tens of minutes. The advantage of the algorithm is the independence of errors from the object heading. It is shown that pre-smoothing of signals improves accuracy. The algorithm makes it possible to determine the orientation angles, and then the direction cosines at a measurement time insufficient to complete the transient evaluation process by the Kalman filter. The conducted field experiments confirm the good characteristics of the alignment on a fixed and vibrating base, achieved using the algorithm.

RESULTS OF SEMI-NATURAL TESTS OF THE AUTONOMOUS DETERMINATION OF THE VEHICLE LONGITUDE AND LATITUDE

Navigation parameters such as vehicle heading, linear speed and latitude and longitude are determined today, as a rule,using satellite navigation systems. However, there is a need for autonomous determination of navigation parameterswhen satellite navigation system signals are unavailable for one reason or another. For example, satellite navigationsystem signals can be jammed by electromagnetic countermeasures.Autonomous systems for determining the vehicle navigation parameters are inertial navigation systems, the basis ofwhich is the double integration of signals from accelerometers, as well as the integration of signals from gyroscopes toform one or another coordinate system, if we are talking about strapdown inertial navigation systems (SINS).Integration of the output signals of accelerometers and gyroscopes, which contain errors, leads to the accumulation ofSINS errors. To increase accuracy, SINS are integrated, for example, with satellite navigation systems. However, suchsystems cease to be completely autonomous.The article uses the method of autonomous determination of vehicle latitude and longitude, which is a new method ininertial navigation. The method is based on the strapdown inertial technology, which, unlike the standard methods usedin SINS, does not use the integration of the output signals of accelerometers and gyroscopes. To determine the angles oflatitude and longitude, the signals of the gyroscopes included in the inertial measurement unite (IMU) are used.As a result of a semi-natural experiment, the efficiency of the method of autonomous determination of latitude andlongitude was verified. Flight data of a small-sized aircraft were used for experimental verification. The data containedinformation about the angles of the heading, pitch, roll of the aircraft, the output signals of three micromechanicalgyroscopes, three micromechanical accelerometers, information about latitude, longitude and altitude measured by thereceiver of the satellite navigation system, as well as information about the horizontal and vertical components of speedand current time. Plots of calculated values of latitude and longitude and values measured using the receiver of thesatellite navigation system are given. From the given plots and tables, it can be seen that from a certain time, thecalculated values coincide with the "reference" ones, which confirms the efficiency of the method of autonomousdetermination of latitude and longitude.

Improved decentralized GNSS/SINS/odometer fusion system for land vehicle navigation applications

Due to low cost and complementary performance advantages, global navigation satellite system (GNSS)/ strapdown inertial navigation system (SINS) integrated systems have established themselves in certain areas of land vehicle navigation. However, this integrated system cannot maintain reliable positioning solutions in challenging environments due to the inherent fragility of GNSS signals and time accumulated errors of a stand-alone SINS. To address this challenge, a multi-source information fusion system based on the decentralized system architecture and sequential Kalman filter for a land vehicle is proposed, which can fuse information from an odometer and motion aided constraints selectively and adaptively in different driving environments. Moreover, a comprehensive calibration and compensation strategy is designed to enhance the information fusion. On the one hand, a real-time calibration algorithm is designed to estimate the time-varying odometer scale factor and the misalignment between the inertial measurement unit (IMU) and vehicle body frame when GNSS signals are available. On the other hand, the forward velocity error caused by the lever arm, and the non-zero lateral velocity generated by the turning maneuver are compensated by the introduced velocity compensation method. A real car experiment in urban areas is carried out to illustrate the effectiveness of the proposed system. It shows that the proposed decentralized GNSS/SINS/odometer fusion system can maintain an average horizontal positioning root mean square error (RMSE) of 1-meter level when GNSS signals are cut off about 1-2 min. In addition, compared with the traditional centralized fusion structure, the proposed decentralized fusion structure can mitigate the horizontal positioning RMSE of the whole trajectory from 2.95 m to 0.59 m, which verifies that it can obtain better performance for the application of low-cost sensors in complex GNSS environments.

Новые методы и средства подготовки карт для навигации по геофизическим полям Земли

The article deals with the matters of creating an integrated navigation system using measurements of the Earth`s gravitational and magnetic fields’ parameters (EGF and EMF). Such a system is to contain a strapdown inertial navigation system (SINS), as well as sensors for the current parameters of the EGF and EMF. Its simulation was carried out. As a result it was found out that, due to the use of information on the parameters of the EGF and EMF, an error of several hundred meters can be obtained. Based on the results of full-scale tests of the created integrated navigation system for the EGF and EMF, a real error of 900 m was obtained with a correction interval of SINS readings of 4 minutes. According to the results of modeling and testing of the mentioned system, the main tasks and ways to solve them are identified. The requirements for measurers of geophysical fields’ parameters and navigation charts are considered, a number of new navigation meters, new methods and tools for preparing navigation charts are proposed. The ways of developing relativistic geodesy and the possibility of using the gravitational-wave astronomy achievements in gravimetry are considered.

Mathematical models of errors of determination of a ground-based SINS orientation angles and latitude of its position

In the paper, a computational algorithm for determination of orientation angles and latitude of position of a strapdown inertial navigation system (SINS) which is mounted on a ground-based transport object is presented. It is assumed that the object is motionless with respect to the Earth’s surface. In the paper, mathematical models of errors of determination of the named angles are also presented. These errors, in their turn, are caused by null shifts and scale factor errors of the SINS sensitive elements – for example, laser gyros and linear accelerometers.

A Tightly Integrated Navigation Method of SINS, DVL, and PS Based on RIMM in the Complex Underwater Environment

Navigation and positioning of autonomous underwater vehicles (AUVs) in the complex and changeable marine environment are crucial and challenging. For the positioning of AUVs, the integrated navigation of the strap-down inertial navigation system (SINS), Doppler velocity log (DVL), and pressure sensor (PS) has a common application. Nevertheless, in the complex underwater environment, the DVL performance is affected by the current and complex terrain environments. The outliers in sensor observations also have a substantial adverse effect on the AUV positioning accuracy. To address these issues, in this paper, a novel tightly integrated navigation model of the SINS, DVL, and PS is established. In contrast to the traditional SINS, DVL, and PS tightly integrated navigation methods, the proposed method in this paper is based on the velocity variation of the DVL beam by applying the DVL bottom-track and water-track models. Furthermore, a new robust interacting multiple models (RIMM) information fusion algorithm is proposed. In this algorithm, DVL beam anomaly is detected, and the Markov transfer probability matrix is accordingly updated to enable quick model matching. By simulating the motion of the AUV in a complex underwater environment, we also compare the performance of the traditional loosely integrated navigation (TLIN) model, the tightly integrated navigation (TTIN) model, and the IMM algorithm. The simulation results show that because of the PS, the velocity and height in the up-change amplitude of the four algorithms are small. Compared with the TLIN algorithm in terms of maximum deviation of latitude and longitude, the RIMM algorithm also improves the accuracy by 39.1243 m and 26.4364 m, respectively. Furthermore, compared with the TTIN algorithm, the RIMM algorithm improves latitude and longitude accuracy by 1.8913 m and 11.8274 m, respectively. A comparison with IMM also shows that RIMM improves the accuracy of latitude and longitude by 1.1506 m and 7.2301 m, respectively. The results confirm that the proposed algorithm suppresses the observed noise and outliers of DVL and further achieves quick conversion between different DVL models while making full use of the effective information of the DVL beams. The proposed method also improves the navigation accuracy of AUVs in complex underwater environments.

The development and evaluation of a combined initial alignment algorithm for strapdown inertial navigation system

This paper proposes a combined initial alignment algorithm for strapdown inertial navigation system, in which the initial alignment is carried out in two stages during the process of preflight preparations while the aircraft is not moving. This method of initial alignment has the advantages of being conducted based on the Earth’s equatorial (Greenwich) coordinate system, which is also used by the aircraft onboard navigation system. In the first stage, the coarse initial alignment is carried out, and this is done according to the orientation parameters using the vector matching method. Then, in the second stage, a fine procedure is carried out, which may be looked at as an integrated system implemented using optimal Kalman filter. In this case, the initial parameters of orientation and navigation are refined, as well as the systematic errors of the inertial sensors are estimated and corrected. The simulation results show that the required level of initial alignment accuracy could be achieved by applying coarse and fine initial alignment procedures not sequentially but in a combination.

Calculation method for deflection of the vertical based on path gravity anomaly compensation

To solverole inefficiency and complete autonomy issues for deflection of the vertical (DOV) determination in geomatics, navigation, and military weapon launching fields, a calculation method based on path compensation has been proposed. After conducting the discretization of the Vening-Meinesz formula in the spherical surface, a novel path gravity anomaly compensation based on the ‘remove–restore’ algorithm was formulated to satisfy the requirement of regional high-precision gravity anomaly data. Meanwhile, utilizing the European Improved Gravity model of Earth by New techniques (EIGEN-6c4), the DOV was calculated using the Vening-Meinesz formula based on the ‘remove–restore’ method, unlike the previous regional direct integral calculations. Finally, to verify the reliability and feasibility of the algorithm, the gravity measurement system, consisting of the high-precision gravimeter and strapdown inertial navigation system, was constructed to conduct the simulation and shipborne experiments. The simulation and experiment results of the proposed method show that the root mean squares, relative to the real values, of DOV calculation components, are 0.89″ and 1.67″, while standard deviation values of the DOV components are the same within 2″ in experiments, which satisfies the precision requirement of the DOV.

A Novel ML-Aided Methodology for SINS/GPS Integrated Navigation Systems during GPS Outages

To improve the navigation accuracy for land vehicles during global positioning system (GPS) outages, a machine learning (ML) aided methodology to integrate a strap-down inertial navigation system (SINS) and GPS system is proposed, as follows. When a GPS signal is available, an online sequential extreme learning machine with a dynamic forgetting factor (DOS-ELM) algorithm is used to train the mapping model between the SINS’ acceleration, specific force, speed/position increments outputs, and the GPS’ speed/position increments. When a GPS signal is unavailable, GPS speed/velocity measurements are replaced with prediction output of the well-trained DOS-ELM module’s prediction output, and information fusion with the SINS reduces the degree of system error divergence. A land vehicle field experiment’s actual sensor data were collected online, and the DOS-ELM-aided methodology for the SINS/GPS integrated navigation systems was applied. The simulation results indicate that the proposed methodology can reduce the degree of system error divergence and then obtain accurate and reliable navigation information during GPS outages.

Novel Special Orthogonal Group Optimization for Coarse Alignment Method of SINS on a Rocking Base

In order to solve the coarse alignment problem of the strapdown inertial navigation system on a rocking base, a fast coarse alignment method using the Special Orthogonal Group optimization has been proposed in this paper. In this method, based on the alignment idea of tracing gravitational apparent motion in inertial frame, the model of coarse alignment on a rocking base has been established using the Special Orthogonal Group directly. A new attitude error function has been proposed on the basis of the cosines between the measurement vector and predictive vector to describe the error between the estimated attitude and the true one. In order to directly reflect the change in the attitude error in the new innovation term and enable the attitude error to converge to zero as fast as possible, the gradient of the new attitude error function has been selected as the new innovation term to compensate for the attitude in the estimation process. Finally, the stability of the proposed optimization estimation method has been proved by employing the Lyapunov stability theory. Simulation and experiment results show that the method presented in this paper exhibits good performance in terms of alignment accuracy and time and can be applied to coarse alignment under a rocking base under different environments.

A GNSS Aided Calibration Method for DVL Error Based on the Optimal-REQUEST

The calibration of scale factor and installation errors plays an essential role to improve the accuracy of integrated navigation system of strap-down inertial navigation system (SINS) and Doppler velocity log (DVL). The DVL measurements will be affected by complex underwater environment, which may affect the calibration results. To solve the above problems, a Global Navigation Satellite System (GNSS) aided separate calibration method for DVL error is proposed in this article. First, an iterative method based on adaptive weight is proposed to calculate the scale factor. Second, a vector observation model is established and the method optimal-REQUEST is introduced to calculate the misalignment angles. In order to improve the estimation accuracy, a vector reconstruction method based on robust filtering is designed according to the measurement variation characteristics of parameter model to reduce the influence of thick tail noise. Finally, the simulation and Yangzi River experiments are conducted to evaluate the feasibility of the proposed DVL calibration method. Experimental results show that the proposed method has a good effect on the calibration of DVL error when DVL measurements are disturbed by noise. Compared to the combined calibration method based on state estimation, iterative method has no need to establish the system noise model, and the estimation result is relatively stable. The river results show that the proposed method is efficient and the positioning result has been greatly improved after calibration.

Pose Measurement Technology of Roadheader Body based on Fusion of Visual and SINS*

In order to solve the problem of pose measurement of roadheader body in low illumination and high dust environment in coal mine, a pose measurement technology of roadheader body based on visual and strapdown inertial navigation system (SINS) is proposed. By extracting the point and line features and using the monocular vision positioning principle based on the gate three lines, the spatial pose of the roadheader body measured by vision is obtained according to the spatial matrix transformation. At the same time, the spatial pose of the roadheader body measured by SINS is calculated by gyroscope and accelerometer. Then the least square method is used to align the two-sensor data, and the registration data is fused based on the extended Kalman filter fusion algorithm to obtain the final fusion roadheader pose data. The experimental results show that the measurement error of the roadheader body position in the x axis direction of the roadway coordinate system is less than ± 20 mm, the error of the y axis direction is less than ±35 mm, the error of the z axis direction is less than ± 10 mm, the yaw angle error is between-0.4° to 0.7°, the roll angle error is between -0.2° and 0.6°, and the pitch angle error is between -0.02° and 0.5°. This method can be used to measure the pose of the roadheader stably and accurately in the complex environment of high dust, miscellaneous light and low illumination.

Analysis of DOV estimation in initial alignment based on Single-axis rotating SINS

The Strapdown inertial navigation system (SINS) requires the precise attitude, whereas the deflection of the vertical (DOV) is normally ignored in alignment. To solve the issue of orientation and position errors caused by DOV, a novel DOV calculation method, estimated by misalignment angles based on single-axis rotating modulation, is proposed. The theoretical limit error equation of attitude angle, affected by the coupling of inertial measurement units (IMU) errors and DOV, has been specifically derived based on the inertial frame alignment theory. It is pointed out that the DOV components directly affect the values of misalignment angles, coupling with horizontal accelerometer errors. Moreover, the specific process of combining coarse alignment of inertial frame and fine alignment of Kalman filter method is presented. Finally, the experiment analysis validates the performance of the proposed method and correctness of the theoretical analysis, where the estimation accuracies of DOV components are 0.349′′ and 0.479′′ respectively.

Online Calibration Method of DVL Error Based on Improved Integrated Navigation Model

For the strap-down inertial navigation system (SINS)/Doppler velocity log (DVL) integrated navigation system, the installation error angle and the DVL scale factor error are the key factors affecting the accuracy of the integrated navigation system. Aiming at this problem, based on the traditional Kalman filter calibration method, this article discusses the estimation of calibration parameters under different models. On the one hand, according to the observability of state variables, an improved calibration model is proposed, and the corresponding state equation and measurement equation are given. On the other hand, the traditional calibration model based on DVL 3-D velocity information is abandoned, DVL four-channel beam velocity information is introduced, a tight integrated calibration model is established, and a tight integrated model calibration method is proposed. Combined with the traditional calibration model and the two improved models in this article, the River test is designed. The experimental results show that the improved model proposed in this article has better calibration accuracy than the traditional model. At the same time, the integrated navigation results based on the SINS/DVL show that the model proposed in this article has a smaller position error.

Robust Initial Alignment Based on Unscented Information Kalman Filter

The velocity of the odometer (OD) and Doppler velocity log (DVL) is easily contaminated by non-Gaussian noise, and the prior information of the state vector is usually hard to estimate accuracy enough, which will lead to the performance of alignment to decline or even divergent. Considering the error characteristic model has very significant nonlinear characteristics, a robust unscented information Kalman filter (RUIKF) algorithm for dynamic initial alignment is proposed in this article to suppress the influence of interference noise. The RUIKF algorithm first calculates the Mahalanobis distance (MD) between observation and the innovation of observation, and an amplification factor is designed to modify the estimation of the measurement noise covariance matrix and then take the modified measurement noise covariance matrix to update the measurement. The state error is estimated by the RUIKF to modify the strapdown inertial navigation system (SINS) navigation solution. Initial alignment experiments are carried out by the RUIKF and unscented information Kalman filter (UIKF) algorithm, respectively, under the conditions of outliers and mixed Gaussian distribution noise. The results show that the RUIKF can suppress the effect of outliers and non-Gaussian noise when the velocity of the OD (or DVL) is contaminated. The experiment results also verify the feasibility and effectiveness of the RUIKF algorithm on the SINS/DVL/OD integrated dynamic initial alignment.

Study on the Shearer Attitude Sensing Error Compensation Method Based on Strapdown Inertial Navigation System

Intelligent mining is the demand and inevitable development direction of China’s coal industry, and the shearer attitude based on the 3D spatial scale is the essential basic information for realizing intelligent mining at the fully mechanized mining face. The traditional shearer attitude monitoring technology cannot meet the accuracy requirements of attitude sensing of mining equipment at an intelligent working face. In this paper, based on the basic principle of strapdown inertial navigation system (SINS), a real-time solution algorithm for shearer attitude is constructed, and the specific process of SINS coarse alignment and fine alignment is studied. The main error terms affecting the accuracy of SINS are analyzed, and the initial alignment error, installation deviation angle error, and vibration effect error are compensated and analyzed through simulation one by one. According to the working characteristics of the shearer, the coning error and sculling error of the shearer’s SINS are compensated and analyzed through simulation. This paper provides a complete set of theories and methods for improvement of the accuracy of the shearer attitude sensing in underground operation; simulation results also verify that the theories and methods can significantly improve the perception accuracy which can provide theoretical and technical reference for the production state prediction of fully mechanized mining face and intelligent control of underground mining equipment.

An accurate 6-DOF dynamic measurement system with laser tracker for large-scale metrology

To develop a highly adaptable real-time 6-degree-of-freedom (6-DOF) measurement system for large precision engineering, high precision and good dynamic performance are the key problems to be solved. In this paper, a novel 6-DOF total measurement system is proposed, which is composed of a vision-inclination combining pose measurement target sensor (PMTS), a built-in strapdown inertial navigation system (SINS) and a cooperative laser tracker as the base station. And a precise mathematical model is established by the measured data from laser tracker, PMTS and SINS to the 6-DOF variables output. Meanwhile, a turntable-based calibration method for the PMTS and the inertial measurement unit (IMU) is proposed. More importantly, to solve the fatal problem that the inclinometer failed under dynamic conditions, a Fault-tolerant Kalman filter (FKF) based on residual chi-square test is introduced. Finally, comparative experiment with Leica T-MAC is conducted to verify the precision and dynamic performance of proposed methods.