Search

Gyroscope biases may drift in long-term storage, and the drifts become the most important factors that affect the azimuth alignment accuracy of the inertial navigation systems (INSs). In this article, a three-position in- field calibration method is proposed, providing a completely new way to solve the gyroscope bias estimation problem without the turntable available. Based on the analysis of the azimuth errors of attitude tracking, the attitude tracking information at the current position is used to extract the azimuth error of self-alignment at the previous position. The observation equations are derived, with the input arguments of the differences between the attitude tracking results and the self-alignment results at the last two positions, and the solution vectors of the gyroscope biases. Condition number is used to analyze the ill-conditioned problem of the equations. Simulation and experiment results show that the gyroscope biases can be effectively calibrated by designing the rotation angles reasonably. [ABSTRACT FROM AUTHOR]

In this article, we proposed a graduated nonconvexity (GNC) aided outlier mitigation method for the improvement of the visual-inertial integrated navigation system (VINS) to face the challenge of dynamic environments with numerous unexpected outlier measurements. A GNC optical flow algorithm was proposed for the detection of the outliers of feature tracking in the front-end of VINS by iteratively estimating the optical flow and the optimal weightings of feature correspondences. Then the feature correspondences with small weightings were excluded. However, excessive outlier exclusion may cause insufficient constraints on the state, causing degeneration of VINS. To solve the problem, this article proposed to detect the potential degeneration based on the degree of constraint in different directions of the pose estimation. Then the number of features being considered was intelligently adapted based on the degeneration level to improve the geometry constraint in the coming epochs. We evaluated the effectiveness of the proposed method by using two challenging datasets (including challenging night scenarios) collected in urban canyons of Hong Kong. The results show that the proposed method can effectively reject the potential outlier visual measurements, and alleviate the degeneration, leading to improved positioning performance in both evaluated datasets. [ABSTRACT FROM AUTHOR]

Autonomous land navigation has been studied extensively in recent years to improve vehicle location accuracy in the satellite-denial environment. However, many previous studies of strapdown inertial navigation system/odometer (SINS/ODO) integrated navigation mainly focus on the horizontal location and neglect the height positioning since the troublesome problem of vertical channel divergency without extra height information assistance. In contrast, this article considered both horizontal and vertical positioning accuracy by SINS/ODO/laser doppler velocimeter (LDV)-based integration framework and proposed a novel algorithm named Schmidt ST-EKF which takes advantage of both ST-EKF’s consistency by nonlinear error definition and Schmidt filter’s improvement on system observability. Detailed system equations and observation equations of Schmidt ST-EKF-based SINS/ODO/LDV integrated navigation are derived and followed by concrete experiment results, which showed that the SINS/ODO/LDV integration method had better horizontal location accuracy than SINS/ODO integration by 35% and SINS/LDV integration by 26% and that applying Schmidt ST-EKF could efficiently reduce height positioning error. A further experiment about the correlating time of pitch mounting angle’s influence is conducted and revealed that the Schmidt ST-EKF is more robust than ST-EKF. Consequently, the algorithm proposed in this article for SINS/ODO/LDV has strong engineering applicability in the vast class of land vehicle applications. [ABSTRACT FROM AUTHOR]

For strapdown inertial navigation systems (SINSs) equipped with high-precision fiber optic gyroscope whose outputs are angular rate signals, the attitude calculation accuracies of traditional angular-rate-based rotation vector algorithms are limited when the vehicle is in a highly dynamic environment due to the fact that only the compensation of the second-order noncommutativity error is considered. Although the attitude algorithms based on polynomial iteration have higher accuracy, the computational complexity is too large. In order to fully tap the accuracy of the inertial measurement unit (IMU) to reduce the attitude errors, the third-, fourth-, and fifth-order Picard solutions for the equivalent rotation vector differential equation are derived in this article. A new method with a compressed structure to calculate the rotation vector for real-time processing systems is proposed to further reduce the noncommutativity error. In a classical coning motion environment, the frequency-series-based Taylor expansions are utilized for error analysis and design of the new algorithm’s coefficients. The effectiveness of the proposed coning algorithm with eighth-order accuracy is validated by digital simulation and initial alignment experiments in the turntable. In comparison with the traditional algorithms, the proposed algorithm improves the yaw alignment accuracy by 78.5%, the pitch alignment accuracy by 73.8%, and the roll alignment accuracy by 66.9% on average. [ABSTRACT FROM AUTHOR]

The scheme of three-self fiber optic gyroscope (FOG) inertial navigation system (INS) has been explored as a potential alternative to provide the desired self-calibration property, and FOG is the main component of the corresponding INS. However, the existence of FOG dead band will cause a severe INS accuracy degradation in process of gyro bias calibration and alignment accuracy. In this article, a correspondence was derived, that is, the ratio of the intersection angle between the FOG input axis and the east direction to the theoretical output of FOG is a constant value in various approximately eastward. Nevertheless, when the intersection angle changes, the ratio also would significantly change because of FOG working in the dead band. Thus, a dead band self-test method at the system level was proposed and the corresponding verification experiments were carried out. The experimental results indicate that the dead band of the $y$ -axis FOG is accurately tested as −0.0125°/h by the proposed method. In conclusion, our work opens up a new perspective regarding the self-test method of the dead band at the system level, and the innovative self-test method is effective and feasible. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) sensor is attracting significant attention in the field of smart transportation because of its ability to give depth information. It is already extensively used for obstacle detection. The inertial navigation system (INS) with a global navigation satellite system (GNSS) gives the global position, orientation, and velocity of a given object in real time. In autonomous driving, LiDAR point clouds are segmented to find out the positions of various objects in the surrounding of the vehicle of interest. However, the obstacle position is determined in local east–north–up (ENU) coordinates. In this article, an end-to-end framework has been presented, which takes input from LiDAR and INS/GNSS systems and gives the obstacle position in universal coordinates (latitude, longitude) in real time. The proposed framework includes ground point removal from raw LiDAR data, obstacle segmentation, and LiDAR data fusion with INS/GNSS data for georeferencing. For LiDAR data processing, a novel method for removing ground points has been presented, in which the entire point cloud is divided into square grids in the horizontal plane. Based on the statistics of the vertical distribution of the points in each grid, ground points are identified. Two ground point datasets were also created for testing the proposed algorithm. These datasets contain point clouds of ground with varying inclination from various multichannel LiDARs. The proposed ground removal method was tested on the Paris-Lille-3D dataset and the dataset created. An F1-score of greater than 0.99 and 0.98 was achieved on our dataset and the Paris Lille-3D dataset, respectively. The framework is tested on different hardware configurations and found to be suitable for real-time applications. [ABSTRACT FROM AUTHOR]

There exist two structures of tri-axis rotational inertial navigation systems (TRINSs), namely, the outer-azimuth and inner-azimuth systems. Accurate calibration is one of the key endeavors toward high-precision TRINS. Regarding the TRINS calibration, there is a strict requirement of the navigation platform definition for the rotation excitation sequence. It means that one rotation excitation sequence cannot be applied to both outer-azimuth and inner-azimuth TRINSs. In this article, a virtual platform is constructed that is different from the usual navigation platform, the virtual platform is obtained by performing specific orthogonal transformation according to the requirements of the application scenario: one is to perform an orthogonal transformation on the $g$ -frame and $a$ -frame, and the other is to perform an orthogonal transformation on the $p$ -frame. Based on the self-calibration rotation excitation sequence of the inner-azimuth TRINS, the Kalman filtering is employed to estimate the error parameters in the virtual platform that are then used to calibrate the outer-azimuth TRINS. Real tests with an outer-azimuth TRINS show that the proposed calibration method achieves the scale-factor accuracy of 0.51 ppm and installation accuracy of $0.11''$ for gyroscopes, and the scale-factor accuracy of 1.56 ppm, and the installation accuracy of $0.17''$ for accelerometers, the proposed method can improve the observability of self-calibration. [ABSTRACT FROM AUTHOR]

The pedestrian navigation system using inertial and magnetic sensors can determine the pedestrian’s attitude and position. However, magnetic field measurement is affected by external magnetic interference, which leads to heading error. Therefore, it is of great importance to suppress magnetic interference. The magnetic threshold method is often used to detect magnetic interference. However, this method fails under the weak magnetic interference environment because the magnetic field intensity measurement is approximately equal to that of the geomagnetic intensity. A generalized likelihood ratio test (GLRT) is proposed in this study. A likelihood ratio function is constructed to maximize the phase probability of magnetic interference so that the inequality relation for detection can be determined. In addition, zero velocity update (ZUPT) algorithm including extended Kalman filter (EKF) or extended Kalman particle filter (EKPF) can suppress accumulated errors, but the heading deviation cannot be accurately estimated. Therefore, an improved EKPF is designed to compensate heading observation by adding adaptive parameters. In order to verify the advantages of the proposed method, the pedestrian navigation system is established and experiments are performed. The false detection rate of the proposed GLRT method decreased by 14.36% compared with the conventional method. Furthermore, the positioning error is reduced by improved EKPF, compared with EKF and EKPF. Therefore, the methods proposed in this study improve the heading and positioning accuracy under a magnetic interference environment. [ABSTRACT FROM AUTHOR]

The integrated navigation of strap-down inertial navigation system (SINS) and Doppler velocity log (DVL) has a common application in the positioning of autonomous underwater vehicle (AUV). However, DVL may be interrupted for a short time when part of the beam cannot receive the reflected signal and DVL measurement information is easily affected by underwater complex environment and contains outliers. To solve the above problems, a Doppler shift-aided coupled method of SINS/DVL integrated navigation with pare of DVL beam measurements outages based on dual adaptive factors is proposed. In this article, a tightly coupled approach for SINS/DVL based on Doppler shifts is proposed to solve the short-term failure problem caused by the lack of part measurement in DVL. Then, a chi-square detection-aided dual factors’ adaptive filter is used to suppress the outliers. The dual factors are used to adjust the influence of imprecise information of dynamic model and observation model error. The chi-square detection is used to judge the outliers of measurements and the result of judgment is regarded as the precondition of factor selection. In order to verify the robustness of the proposed algorithm to outliers, simulation and Yangtze River experiments are carried out. The results of simulation and river experiments indicate that the tightly coupled approach obtains higher accuracy compared with the loosely coupled model and the proposed adaptive filter can effectively suppress the outliers. The method proposed in this article can achieve greater robustness in AUV underwater complex environment. [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]

Aiming at the problem of outliers in Doppler velocity log (DVL) beam measurements, for strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) tightly coupled system, an improved robust Kalman filter (KF) algorithm is proposed, which can handle the measurement noise variance of each DVL beam individually. First, the multidimensional measurement equation is decomposed into several 1-D measurement equations, i.e., different DVL beam measurements are assumed to be obtained by different sensors. Second, the statistical similarity measure (SSM) is introduced to quantify the similarity between two random vectors. And a new cost function based on SSM is constructed for the dynamic system containing one state equation and multiple measurement equations. To optimize this cost function, the iterative procedures to update the posterior state is derived based on the Gauss-Newton iteration algorithm. Both the simulation and lake trial illustrate the superiority and effectiveness of the proposed algorithm. It is shown that in the case of partial DVL beams with outliers, the proposed algorithm can avoid the loss of normal measurement information and thus improve estimation accuracy. [ABSTRACT FROM AUTHOR]

At the near-vertical position, the system error is a major factor lowering the attitude accuracy in the attitude determination system, and correction technology is often used to compensate all kinds of system errors. First, the absolute errors of the computed attitude angle including the measurement error and the correction error are analyzed, and the correction error caused by the correction matrix is emphatically derived and discussed. Then, a novel balance correction method with a nonunit correction matrix is proposed and verified theoretically. Finally, an inertial measurement unit (IMU) is tested for the near-vertical position and corrected with the conventional unit matrix and the proposed balance correction. The experimental results suggest that the balance correction under the near-vertical position can enhance the accuracies of the attitude angles over four times than those with the conventional correction. [ABSTRACT FROM AUTHOR]

The zero-velocity update (ZUPT) technique plays a key role in foot-mounted inertial navigation system, which can minimize the drift errors of low-cost inertial measurement unit (IMU) by resetting the velocity to zero when the foot is stationary. However, it is difficult to detect the zero-velocity interval (ZVI) in mixed locomotion patterns. This article presents a novel ZVI detector based on adaptive simulated energy consumption (SEC) curve. It can identify the ZVIs of various locomotion patterns, such as normal walking, fast walking, running, going upstairs, and downstairs. The SEC curve is generated based on the signal processing approach using the interlaced peak property of the IMU-measured angular rate and acceleration waveforms. Another benefit of the proposed method is that the IMU can be mounted on the instep without restrictions of orientation and position, which improves flexibility and convenience. Experiment results show that the proposed algorithm performs better than three other widely used methods in mixed gait patterns. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicle (AUV) mostly relies on an integrated navigation system, which consists of a strap-down inertial navigation system (SINS) and Doppler velocity log (DVL). The integrated system provides continuous and accurate navigation information when compared to stand-alone SINS or DVL. However, the dependence of DVL signals on the acoustic environment may cause any DVL malfunction due to marine organisms or strong wave-absorbing material. This article introduces a novel method utilizing Dempster–Shafer (DS) theory augmented by least squares support vector machines (LSSVMs) known as DS-LSSVM. The SINS and DVL data fusion are designed by DS theory whereas LSSVM models the SINS error. The virtual DVL is built by the proposed DS-LSSVM method, which makes AUV navigation purposes possible during the long-term DVL outage. The test results demonstrate the effectiveness of the proposed virtual DVL signal estimation method. In addition, the positioning accuracy of the proposed method outperforms that of the LSSVM method. [ABSTRACT FROM AUTHOR]

For a strap-down inertial navigation system (SINS), a traditional navigation frame switching mechanization can be used to solve the all-Earth navigation problem, however, this kind of method suffers from complex and discontinuous algorithms. On the basis of improving vertical channel damping technology, a complete and unified all-Earth navigation scheme arranged in the Earth-centered Earth-fixed (ECEF) frame is designed as an alternative for global applications, which results in a simpler continuous algorithm and lower computational complexity. We also propose a virtual polar region technology based on the grid attitude and velocity invariant method (G-AVIM), which can complete the polar navigation verification through the actual test data of nonpolar regions. In the reconstructed polar region trajectory, the vehicle attitude, velocity, and height information relative to the ground remain unchanged, so the real test environment can be completely reproduced in the polar region. Simulations and field test results verified the proposed methods. [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]

To enhance the performance of the inertial navigation system (INS)/global position system (GPS) integrated navigation system for the land vehicle during GPS outages is an extremely challenging task. Though existing researches have made reasonable progress in positioning accuracy, they largely ignore sophisticated vehicle stopping events, and the further improvement of positioning performance is urgently needed in complex urban environments. In this article, we propose a heterogeneous multi-task learning (MTL) structure with a shared de-noising process to conduct pseudo-GPS position prediction and zero-velocity detection. The raised model builds upon three vital parts: 1) a shared de-noising convolutional autoencoder (CAE), which can effectively filter the measurement noises in the original inputs and provide more clean data for subsequent calculations without the ground-truth sensor data; 2) a predictor that uses a deep temporal convolutional network (TCN) to predict pseudo-GPS position to bridge GPS gaps; and 3) a robust zero-velocity detector that utilizes a 1-D deep convolutional neural network to accurately detect the vehicle stationary pattern, allowing for timely correcting the velocity and heading. Our proposed MTL model is evaluated on extensive practical road data and achieves a root mean square error of 3.794 m for 120-s GPS outages under long-term vehicle stopping scenarios, which obviously outperforms the stand-alone long short-term memory, TCN, and TCN + CAE. Experimental results also demonstrate that our proposed MTL method yields a remarkable accuracy of over 99.0% for vehicle stationary detection. [ABSTRACT FROM AUTHOR]

In the recent years, the availability of accurate vehicle position becomes more urgent. The global navigation satellite systems/inertial navigation system (GNSS/INS) is the most used integrated navigation scheme for land vehicles, which utilizes the Kalman filter (KF) to optimally fuse GNSS measurement and INS prediction for accurate and robust localization. However, the uncertainty of the process noise covariance and the measurement noise covariance has a significant impact on Kalman filtering performance. Traditional KF-based integrated navigation methods configure the process noise covariance and measurement noise covariance with predefined constants, which cannot adaptively characterize the various and dynamic environments, and obtain accurate and continuous positioning results under complex environments. To obtain accurate and robust localization results under various complex and dynamic environments, in this article, we propose a novel noise covariance estimation algorithm for the GNSS/INS-integrated navigation using multitask learning model, which can simultaneously estimate the process noise covariance and measurement noise covariance for the KF. The predicted multiplication factors are used to dynamically scale process noise covariance matrix and measurement noise covariance matrix respectively according to the inputs of raw inertial measurement. Extensive experiments are conducted on our collected practical road data set under three typical complex urban scenarios, such as, avenues, viaducts, and tunnels. Experimental results demonstrate that compared with the traditional KF-based integrated navigation algorithm with predefined fixed settings, our proposed method reduces 77.13% positioning error. [ABSTRACT FROM AUTHOR]

The initial alignment process is of vital importance to the strapdown inertial navigation system (SINS), and SINS is consisting of the inertial sensors. In this article, the aiding velocity of Doppler velocity log (DVL) is used to implement the in-motion alignment process. However, the measurements of DVL are easy to corrupt by the outliers, and the outliers will degrade the performance of the initial alignment process. Thus, a robust in-motion alignment method for the SINS/DVL integrated navigation system is proposed in this article. First, the vector construction with the measurements of SINS and DVL is introduced. Then, the discrete form of the vector by considering different sampling rates between SINS and DVL is derived in detail. By analyzing the relationship between observed and reference vectors, the magnitude matching method is devised for outliers detecting. Meanwhile, Huber’s robust theory is used to calculate the weighted value with the magnitude matching method. Using the weighted value, the filter-QUEST attitude determined method is modified to implement a robust attitude determination. Finally, the simulation and field tests are designed to validate the performance of the proposed method. The alignment results demonstrate that the proposed method has similar accuracy with the current popular method in which the measurements of DVL are not corrupted by the outliers. [ABSTRACT FROM AUTHOR]

Rotational inertial navigation system (RINS) could improve the navigation performance by rotating the IMU around gimbals, and the structure and function of the dual-axis RINS also help realize the process of self-calibration. However, the gyro–accelerometer asynchronous time is not considered in current studies. Coupled into the navigation error, the gyro–accelerometer asynchronous time can lead to velocity accuracy decline during the process of rotation. Therefore, the modeling and calibration of the gyro–accelerometer asynchronous time would enhance the systemic reliability. In this article, in order to solve the abovementioned problem: 1) we model the gyro–accelerometer asynchronous time and perform an error analysis of gyro–accelerometer asynchronous time; 2) we propose a compensation method of the outer lever arm to improve the accuracy of the observed velocity and position; and 3) we derive a continuous linear 40-D RINS error model considering the abovementioned two error parameters compared with the traditional method, the PWCS method, and the SVD method is utilized to provide the observability degrees of the proposed state during the self-calibration process. Experimental results illustrated that the proposed method can improve the accuracy in the long-endurance navigation. [ABSTRACT FROM AUTHOR]

In many practical applications, the initial alignment of the strapdown inertial navigation system (SINS) must be performed under large misalignment angles. However, the error model of SINS in this situation is nonlinear, making it hard to obtain an accurate initial alignment result. Aiming at solving this problem via nonlinear filtering, an improved Markov chain Monte Carlo (MCMC)-based particle filter (PF) algorithm is designed in this article, which uses a square-root cubature Kalman filter (SCKF), PF, and MCMC algorithm synthetically. The algorithm is dedicated to extract particles from the posterior distribution of the state by the improved MCMC algorithm and avoid the particle degeneration and the particle impoverishment in a PF. This can effectively improve the performance of particle filtering for nonlinear systems. Furthermore, to solve the problem caused by the fixed number of iterations in the typical MCMC algorithm, a novel adaptive approach is introduced according to the maximum mean discrepancy. The simulation and experimental results of GPS-aided SINS in-motion initial alignment show that the proposed algorithm can provide good filtering accuracy and have good real-time performance. [ABSTRACT FROM AUTHOR]

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]

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

Sensor fusion of inertial navigation system (INS) and ultrawideband (UWB) technology is an effective approach to enhance the accuracy and robustness of indoor pedestrian tracking system. The main drawbacks of state-of-the-art approaches are the number of required UWB anchors and poor system scalability in terms of cost-effectiveness when covering a large indoor area. In this article, an INS and UWB fusion system using particle filter (PF) for pedestrian tracking is proposed. The system utilizes only distance measurements from UWB anchors and incorporates a particle resetting approach. The resetting approach aims to address the lost track problem in PF, which limits the long-term reliability of PF-based fusion tracking systems. The developed pedestrian tracking system is able to provide accurate and long-term robust tracking performance, and at the same time, possesses high scalability to cover a large area of indoor environment using sparsely deployed UWB anchors. A practical experiment is conducted with test path length over 500 m to demonstrate the effectiveness of the proposed approach, which is able to reduce the mean position error by 39.42% when compared to INS only result. [ABSTRACT FROM AUTHOR]

Special approaches are required to integrate low-cost MEMS-based inertial navigation system (MEMS-INS) with global navigation satellite system (GNSS). This article proposes a robust GNSS/MEMS-strapdown inertial navigation system (SINS), the tightly coupled navigation approach aided by non-holonomic constraint (NHC). The tight integration model is adopted to overcome the accuracy degradation caused by changing satellite geometry and frequent GNSS outages in urban areas. The innovation-based test statistic is devised to enhance the filtering with the resistance to GNSS outliers. To further improve the stand-alone accuracy during relatively long GNSS outages, the virtual measurement is also introduced using the NHC condition that land vehicle does not slip and always remain in contact with the ground. The field test results with continuous GNSS aiding indicate that the proposed algorithm can improve overall accuracy in position, velocity, and attitude by about 16, 43, and 19% with immunity from GNSS outliers, compared with the traditional loosely coupled (LC) method. Even during 60-s GNSS outages, the proposed algorithm can effectively compensate MEMS-SINS errors, accomplishing the overall accuracy improvements by about 46% (position), 35% (velocity), and 15% (attitude), compared with the traditional LC counterpart. [ABSTRACT FROM AUTHOR]

In general, the strap-down inertial navigation system (SINS)/Doppler velocity log (DVL)-integrated navigation method can provide continuous and accurate navigation information for autonomous underwater vehicles (AUV). This SINS/DVL fusion is the loosely integrated method, in which DVL may contain large error or does not work when some beam measurements are inaccurate or outages for complex underwater environment. To solve these problems, in this article, a novel tightly integrated navigation method composed of an SINS, a DVL, and a pressure sensor (PS) is proposed, in which beam measurements are used without transforming them to 3-D velocity. The simulation and vehicle test show that the proposed method can significantly outperform the traditional loosely integrated method in providing estimation continuously with higher accuracy when DVL data are inaccurate or unavailable for a complex environment. Compared with loosely integrated method, the position accuracy of the proposed method has improved by 32.5%. [ABSTRACT FROM AUTHOR]

In this article, we focus on addressing the nonlinear filtering problem with large prior uncertainty but high measurement accuracy, which may be encountered in the application of tightly coupled global navigation satellite system (GNSS)/inertial navigation system (INS) integration. Although the existing methods, such as progressive Gaussian approximate filter (PGAF), can address this problem, it has poor estimation accuracy. To improve the estimation accuracy of PGAF, the step sizes as well as the measurement noise covariance matrix (MNCM) are jointly estimated based on the variational Bayesian approach, from which a novel PGAF with variable step size is developed. Tightly coupled GNSS/INS integration simulations illustrate that the proposed filter outperforms the existing methods both in estimation accuracy and rate of convergence. [ABSTRACT FROM AUTHOR]

Odometer and nonholonomic constraints are crucial external aids for inertial navigation systems (INSs) to achieve desirable positioning accuracy for pipeline inspection gauges (PIGs). Exploiting the maximum potential of these constraints requires the accurate knowledge of the inevitably existing misalignment in the attitudes of the inertial measurement unit (IMU) with respect to the host vehicle (i.e., the IMU mounting angle relative to the PIG). In this paper, we address the mounting angle calibration of a PIG without using sophisticated equipment. Instead, the proposed method only requires the PIG to be placed on a simple leveled frame and rotated about its longitudinal axis. In this case, the existing IMU mounting angles will make the IMU’s pitch and heading angles vary with rotation. Closed-form analytic solutions for the IMU’s attitude angles are obtained under this specific condition and show that the magnitude and phase of the IMU’s pitch and heading angles have a linear relation with the corresponding pitch and heading misalignment angles. Then, a method for the misalignment angle estimation is presented based on this principle. Simulations are performed to validate the proposed method and investigate the related error sources that influence the estimation accuracy. The results of the real tests indicate that the mounting angles can be accurately estimated and compensated through this simple and convenient calibration. [ABSTRACT FROM AUTHOR]

In industrial environments, the determination of the positions and orientations of tools in complex manual assembly processes allows an automated monitoring about the fulfillment of the process and a quality control. This contribution presents an optoacoustic indoor localization system based on combined distance and inertial measurements that is able to undertake this positioning task. In doing so, an accurate distance measurement is achieved with ultrasound and infrared. This novel system is able to localize multiple moving objects attached to transmitters simultaneously by measuring the unilateral distances to room-fixed receivers and fusing these measurements with inertial navigation system data (acceleration, angular velocity, and magnetic field) in a particle filter (PF). In doing so, the PF is determining the objects’ poses by using the accelerometer and gyroscope data for a pose prediction in a propagation step and by utilizing the distance measurement and magnetometer data for an estimation correction. A commercially available, optical reference system is used to record the dynamics of human hand movements in an assembly scenario in order to analyze these dynamics for a system performance evaluation. Subsequently, the novel, an optoacoustic system is tested by applying these dynamics to an electrical linear axis on which a transmitter is fixed. Compared to the reference system, the median position error achieved is below 2.6 cm and the median orientation error is less than 10.0°, even when going beyond the hand movement dynamics recorded previously. [ABSTRACT FROM AUTHOR]

In this paper, an in-motion coarse alignment based on the optimization-based method and vector observation for strapdown inertial navigation system is proposed. The traditional coarse alignment method converges slowly on the moving base, owing to the measurement noises of the observation measurements. To solve this problem, the method of sliding fixed-interval integration is utilized to construct the observation vector model, which can suppress the noises in the measurements. An optimization-based method named optimal-REQUEST algorithm is used to determine the attitude quaternion, which can further filter the measurement noise by adjusting the gain of the filter adaptively. To verify the performance of the proposed method, an in-motion simulation and vehicle tests are carried out. The results show that the convergence rate of the proposed method is faster than that of the compared methods, and the convergence process of the first one is more stable than that of the others. [ABSTRACT FROM AUTHOR]

This paper investigates the gravity disturbance compensation for inertial navigation system based on high-resolution global gravity field models. The high-precision gravity is calculated using the European Improved Gravity model of the Earth by New techniques (EIGEN)-6C4, which is a representative of the high-resolution models. The coupling effects of horizontal uncalibrated accelerometers drift bias and gravity disturbance on initial alignment and pure calculation have been analyzed in detail. It is pointed out that the gravity disturbance compensation is necessary only when the uncalibrated accelerometers drift bias is smaller than the gravity disturbance with one order of magnitude. Simulation and ship-mounted experimental results validate the theoretical analysis and conclusions. [ABSTRACT FROM AUTHOR]

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

Accelerometers are used in a wide variety of applications including inertial navigation systems and activity monitors. In order to reduce systematic and random errors and improve accuracy and precision, this paper proposes a differential accelerometer system where two triaxial accelerometers are combined in opposite directions. The differential system allows to create additional constraints that can be used for improving calibration and estimation. Calibration methods based on nonlinear optimization are suggested to determine the offset and the scaling vectors. The differential system also permits to determine and update the offset and the scaling factors dynamically based on the system’s constraints. Sensor fusion is accomplished using a weighted least squares method. Experiments are carried out to evaluate and validate the system and the proposed methods including comparison with a single accelerometer system. It is shown that the differential system facilitates and improves acceleration estimation in terms of accuracy and precision. [ABSTRACT FROM AUTHOR]

The self-alignment process for the strapdown inertial navigation system (SINS) is performed mainly to achieve an accurate initial attitude only using the measurements from the inertial measurement unit (IMU). Different from conventional self-alignment methods, a gradient descent optimization-based SINS self-alignment method, which can determine the initial attitude without using the latitude information, is proposed in this paper. According to certain geometry constraints of the earth rate vector in the navigation frame, we use the measurements from the IMU to represent the earth rate vector instead of directly using the latitude information. To overcome the sensor noise disturbance, we also construct a quaternion-based nonlinear objective function with the estimated earth rate vector, which is formulated as seeking the least square solution of a Wahba problem. Thus, we employ the gradient descent optimization to achieve the optimal solution of the nonlinear objective function. The simulation and experiment results verify the alignment performance and flexibility of the proposed self-alignment method for SINS stationary alignment without using a priori local latitude information. [ABSTRACT FROM AUTHOR]

In the integrated inertial navigation system (INS) and celestial navigation system (CNS), the attitudes and positions of probes are estimated based on the auxiliary measurement of an infrared Earth sensor. The poor accuracy of the infrared Earth sensor compared to that of the star sensor significantly limits the navigation accuracy of the integrated navigation system. This paper proposes a novel INS/CNS integrated navigation system that uses the specific force with noncumulative error to correct infrared Earth measurements. The probe azimuth angle and elevation angle relative to the Earth’s inertial frame are first estimated according to the specific force provided by the INS to filter the infrared Earth sensor’s measurement error. Then, the corrected infrared Earth measurement assists the CNS to obtain accurate celestial measurements. The integrated INS/CNS navigation system in the tightly coupled mode finally estimates the accurate probe attitude and position. Compared to the integrated navigation system, which directly uses the uncorrected infrared Earth measurement, the yaw, pitch, and roll angle are improved under the proposed method by 76.73%, 77.64%, and 75.39% on average, respectively; the latitude and longitude estimation errors improve under the proposed method by 70.44% and 61.92% on average, respectively. In short, the proposed method is a feasible and highly accurate approach to the INS/CNS integrated navigation system. [ABSTRACT FROM AUTHOR]

Pedestrian dead reckoning (PDR) using inertial and magnetic measurement unit (IMMU) is a current research hotspot that can be used to provide location-based services. The main challenges of the self-contained sensor-based PDR method are mainly sensor errors and orientation estimation errors. In this paper, a novel orientation estimation algorithm and gait phase detection algorithm with strong adaptability are proposed. The variance and magnitude of the angular rate are adopted to detect the gait. The experimental results show that the gait phase detection method can accurately distinguish the swing phase and the stance phase of the foot even under running conditions. By reasonable assumptions, the attitude angles calculated in the swing phase can be calibrated while the attitude angles are in the stance phase. The calibration of velocity not only uses the zero velocity updates method but also makes reasonable assumptions about the bias error of the accelerometer, so that the velocity calculated during the whole swing phase can be calibrated. Experimental results demonstrate that the proposed self-contained IMMU is capable of providing consistent beacon-free PDR in different scenarios, achieving less than 1.2% average distance error and average end-to-end position error. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel localization methodology to enhance the accuracy from two aspects, i.e., adapting to the uncertain noise of microelectromechanical system-based inertial navigation system (MEMS-INS) and accurately predicting INS errors. First, an interacting multiple model (IMM)-based sequential two-stage Kalman filter is proposed to fuse the information of MEMS-INS, global positioning system (GPS), and in-vehicle sensors. Three bias filters are built with different covariance matrices to cover a wide range of noise characteristics. Then, IMM algorithm provides a soft switching among the three bias filters to adapt to the uncertain noise of MEMS-INS. Further, an elaborate predictor is developed to accurately predict INS errors during GPS outages. The elaborate predictor comprises an online trained autoregressive integrated moving average (ARIMA) model and an offline trained extreme learning machine (ELM) model. The ARIMA model is designed to predict the basic accumulation process of INS errors, while the ELM model is designed to correct the errors caused by the changes of noise characteristics. Thus, the INS errors can be properly compensated when GPS observations are not available. In all, the proposed localization methodology can achieve accurate performance when facing uncertain noises of MEMS-INS and GPS outages simultaneously. To verify the effectiveness of the proposed methodology, road test experiments with various driving scenarios were performed. The experimental results illustrate the feasibility and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Dual-axis rotational inertial navigation system (RINS) has been widely used in long-term marine navigation for recent years. With all constant biases averaged out, those gyro biases related to geomagnetic or gravitational field, which cannot be averaged out, become the main error source. In this paper, the geographic-frame-equivalent (G-F-E) gyro bias is defined by integrating the gyro biases caused by all kinds of external fields. A practicable online calibration method is proposed by deducing the error propagation rules. The calibration equation is established by observing position errors at three points, and the optimal solution of the G-F-E gyro bias is obtained using the least square method. Static and sailing experimental results show that the G-F-E gyro bias can be calibrated during the 6-h initial alignment. The proposed calibration method can further improve the position accuracy of a dual-axis RINS by 30% compared with the previous method when the G-F-E gyro bias changes. [ABSTRACT FROM AUTHOR]

With the development of the ubiquitous pedestrian position in environments where global navigation satellite system is unavailable or there is no preinstalled wireless device for positioning, the use of self-contained sensors becomes more important. Thus, this paper proposed a 3-D pedestrian position system combined with commercial microelectromechanical systems microinertial measurement unit (MIMU) and surface electromyography (SEMG) sensor. The signal of the SEMG is used mainly in two aspects: 1) to improve the recognition rate of motion pattern and 2) to obtain the more accurate 3-D velocity contrasted to MIMU. Therefore, satisfied 3-D position accuracy is obtained in two different terrain environments. [ABSTRACT FROM PUBLISHER]

This paper presents an innovative in-field coarse bias estimation (CBE) method for stationary intermediate-grade inertial measurement units (IMUs), which is based on the existence of normality and orthogonality errors in the attitude matrix supplied by the traditional three-axis attitude determination stationary coarse alignment (CA) stage. Differently from the currently existing in-field bias estimation formulations, the proposed CBE method is conceived to be conducted in stationary conditions, jointly with the CA, and it allows the down accelerometer bias and the north and down angular rate sensor biases to be adequately estimated. As a main contribution of this paper, we demonstrate that the proposed CBE method is able to greatly accelerate the estimation process of the aforementioned biases, contributing, hence, to shorten the IMU setup time. A comprehensive error analysis is presented, and the conclusions are twofold: 1) the proposed CBE method is valid regardless of the IMU orientation and 2) its estimation accuracy is mainly constrained by the north accelerometer bias and the position location uncertainty, which are, in general, very reduced. Results from simulated and experimental test confirm the adequacy of the outlined verifications, also attesting the superiority of the proposed CBE method in situations where, due to time constraints, stationary fine alignment procedures are not allowed to be carried out. [ABSTRACT FROM PUBLISHER]

This paper presents an attitude estimation method from uncertain observations of inertial sensors, which is highly robust against different uncertainties. The proposed method of covariance inflated multiplicative extended Kalman filter (CI-MEKF) takes the advantage of non-singularity of covariance in MEKF as well as a novel covariance inflation (CI) approach to fuse inconsistent information. The proposed CI approach compensates the undesired effect of magnetic distortion and body acceleration (as inherent biases of magnetometer and accelerometer sensors data, respectively) on the estimated attitude. Moreover, the CI-MEKF can accurately estimate the gyro bias. A number of simulation scenarios are designed to compare the performance of the proposed method with the state of the art in attitude estimation. The results show the proposed method outperforms the state of the art in terms of estimation accuracy and robustness. Moreover, the proposed CI-MEKF method is shown to be significantly robust against different uncertainties, such as large body acceleration, magnetic distortion, and errors, in the initial condition of the attitude. [ABSTRACT FROM PUBLISHER]

In-motion alignment of strapdown inertial navigation systems (SINS) without any geodetic-frame observations is one of the toughest challenges for autonomous vehicles. Considering the characteristics of SINS, this paper presents a dual-model-based in-motion alignment method for the odometer-aided SINS. Two inertial navigation calculation loops are established for an inertial measurement unit (IMU), one of which executes the in-motion gyrocompass horizontal alignment algorithm to decompose the body velocity measured by odometer to navigation frame and attenuate the disturbance. And the other is the attitude determination-based alignment loop, where the vector observation-based SINS alignment is executed. The contributions of the work presented here are twofold. First, the dual-model initial alignment (DMIA) algorithm for SINS is proposed, which introduces the idea of constructing multiple calculation loops for an IMU to maximize the advantages of SINS. Second, depending on the body-frame speed-aided attitude determination and navigation-frame speed-aided attitude determination, the body-frame velocity observation with disturbance is accurately decomposed to navigation frame with the noise attenuated by gyrocompass horizontal alignment. The experimental results show that the proposed DMIA algorithm can achieve a rapid and accurate in-motion alignment. [ABSTRACT FROM AUTHOR]

In order to obtain the accurate velocity of a vehicle, calibration of a laser Doppler velocimeter (LDV) is particularly important after installation of the inertial navigation system (INS) and LDV. The calibration accuracy determines the navigational accuracy of the INS/LDV integrated navigation system. In this paper, a highly accurate calibration method based on the error equations of the INS/LDV integrated system is presented. The basic principles of analytic calibration and filtering calibration methods are discussed in detail. The test results show that the scale factor and the installation error of the LDV can be automatically calibrated by our novel calibration method with sufficient accuracy. For a high-precision INS and a 2-D LDV integrated navigation system, after calibration, the position error of dead reckoning (DR) decreased from 1180 m in 2 h to 8 m in 2 h. For a medium precision INS and a 1-D LDV integrated navigation system, after calibration, the position error of DR decreased from 1300 m in 1 h to 25 m in 1 h. The excellent parameter stability of the LDV is also illustrated. [ABSTRACT FROM AUTHOR]

This paper investigates the applicability of a developed Magnetic Positioning System (MPS) as a support for a dead-reckoning inertial navigation system (DR-INS) for pedestrian applications. The integrated system combines the complementary properties of the separate systems, operating over long periods of time and in cluttered indoor areas with partial nonline-of-sight conditions. The obtained results show that the proposed approach can effectively improve the coverage area of the MPS and the operation time with bounded errors of the DR-INS. In particular, a solution that provides bounded position errors of 1–2 m over significantly long periods of time up to 45 min, in realistic indoor environments, is demonstrated. Moreover, system applicability is also shown in those scenarios where arbitrary orientations of the MPS mobile node are considered and an MPS position estimate is not available due to less than three distance measurements. [ABSTRACT FROM PUBLISHER]

Complex and fatal road vehicle crashes are often a matter of dispute in forensic investigation. The proposed algorithms and methods, utilizing data from inertial measurement units and satellite navigation, enable road–vehicle trajectory reconstruction in 3-D suitable for postaccident forensic analysis. This paper provides an estimate of the reliability of this approach and its relation to component specifications. We start by describing an appropriate type of the strap-down inertial navigation system before providing a detailed performance analysis when the system is integrated with global navigation satellite systems. In addition, we present custom hardware and embedded software solutions, as well as experimental evaluation in real-world case study, capturing typical crash scenario performed at an approved safety performance assessment laboratory. In our experiments, strap-down inertial mechanization was used for short-term vehicle trajectory reconstruction using sensor readings and compared with ground-truth position captured on video. We have used reference equipment to validate the approach and the experiment resulted in a positioning accuracy on a decimeter level. [ABSTRACT FROM PUBLISHER]

This paper deals with the problem of lever-arm compensation encountered in an unmanned aerial vehicle surveying system, which is composed of several surveying sensors, a position and orientation system (POS), and an electrooptical pod. As an integration of an inertial navigation system and a global positioning system (GPS), the POS is used to provide the position and attitude of the surveying sensors for motion compensation. However, the lever arm between the inertial measurement unit and the GPS antenna is uncertain and time-varying, and therefore, its influence on the estimation accuracy of POS is serious. In order to improve the measurement accuracy of the POS, a new scheme of dynamic lever-arm compensation is proposed in this paper. The main contribution of this paper is twofold. First, by using the encoder data of the electrooptical pod, a dynamic lever-arm compensation method is derived to compensate the caused lever-arm effect. Second, an H\infty a posteriori filter is designed for POS to attenuate the influence of a lever-arm compensation error and noise uncertainty. Finally, an actual flight experiment for the transmission line corridor surveying is carried out, and it is validated that the proposed method outperforms the existing methods. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of robust fault detection (FD) for an integrated system of inertial navigation system and global position system (INS/GPS). Under the assumption of process disturbance and measurement noise being l2 -norm bounded, a state-space model with potential additive fault is first introduced to describe the error dynamics of the INS/GPS integrated system. For the purpose of the FD, an observer-based FD filter (FDF) is used as a residual generator and the design of the FDF is formulated so as to find an observer gain matrix and a postfilter in the framework of Hi/H_\infty optimization. By utilizing the techniques of innovation analysis and orthogonal projection, a solution to the extended Hi/H_\infty -FDF is given in terms of Riccati recursions. Furthermore, a strategy of residual evaluation is also considered, which concerns the selection of an evaluation function and a threshold. Finally, a flight experiment is implemented to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents an analytic coarse transfer alignment method based on inertial measurement vector matching for the inertial navigation system (INS) on vertically launched missiles from the ground. The alignment problem is transformed into the estimation of the relative attitude between the master INS (MINS) and slave INS (SINS) by decomposing the attitude matrix of SINS. As inspired by the ideology of the traditional analytic alignment method, the measurements from the accelerometers and gyroscopes of MINS and SINS are utilized to construct matching vectors to estimate the relative attitude between them without any initial information. Furthermore, the sensitivity of Euler angles with respect to the inertial sensor errors is analyzed according to the presented coarse transfer alignment principle. An algebraic expression for alignment error estimation is given, and thus the real-time precision evaluation for the presented coarse alignment method is achieved. It allows the system to switch to fine alignment in a timely and accurate manner. The simulation and flight test demonstrate that, compared with the quaternion-optimization-based alignment method proposed by Kang et al., the presented method has a smaller calculation cost and satisfactory convergence. The corresponding precision evaluation algorithm can describe the trend of true alignment error accurately, and is good enough to judge the moment for switching to the fine alignment. [ABSTRACT FROM AUTHOR]

The airborne attitude and heading reference system (AHRS) is an inertial system that provides the aircraft with navigation parameters in real time. A new coarse transfer alignment method is proposed for low-accuracy AHRS with a large misalignment angle between AHRS and master inertial navigation system (MINS). The new coarse alignment method is derived from the quaternion-optimization-based in-flight alignment (IFA) method. By resolving the alignment problem in body frame, the proposed method makes full use of the navigation information of MINS and transforms the alignment problem into a quaternion optimization problem. Its accuracy is improved by presenting a two-step iterative algorithm. Compared with the quaternion-optimization-based IFA method, the proposed method is more robust and accurate by introducing the attitude information of MINS. The simulation and experiment results show that the proposed method is applicable to both large and small misalignment angles, and it serves as a nice coarse alignment that permits the implementation of linear error model in fine alignment process. [ABSTRACT FROM AUTHOR]