Inertial Navigation System Error Research Articles

Environmental information-assisted intelligent fusion localization for vehicles in urban area

In this paper, we propose a localization methodology aimed at improving accuracy through two primary aspects: environment information assisting fusion algorithm and the elaborate localization errors prediction. First, the environment-assisted particle filter (EA-PF) is proposed by enhancing the particle filter algorithm with perceived environmental feature information, which is reflected by the rasterized map constructed based on LiDAR sensors. Then, an elaborate localization error prediction model is designed to accurately forecast Inertial Navigation System (INS) errors. The model comprises the noise characteristic feature extraction and encoding network and multi-feature error prediction network. The localization errors in the EA-PF output can be further compensated. The proposed localization methodology can be divided into two operational modes based on the availability of Global Navigation Satellite System (GNSS) signal: GNSS-available and GNSS-unavailable modes. In the GNSS-available mode, the EA-PF algorithm yields precise localization results, while in the GNSS-unavailable modes, the elaborate localization error prediction model can effectively predict INS errors and provide the compensation for the EA-PF. To validate the effectiveness of the proposed methodology, relevant test experiments were performed using the KITTI dataset. The experimental validation results demonstrate the feasibility and effectiveness of the proposed methodology. The experimental results demonstrate that the EA-PF outperforms traditional PF methods by improving the root mean square error (RMS.) by 32.70 % on average during 90 s GNSS outages. Moreover, the proposed localization methodology achieves 2.30 m RMS error on average during 90 s GNSS outage.

Research on propagation mechanism for gyro installation error of dual-axis rotational inertial navigation system in UAV coordinated U-turn

With the development of rotation modulation, dual-axis rotational inertial navigation systems (RINS) are gradually applied to unmanned aerial vehicles (UAV) to cope with the increasing prominence of GNSS spoofing. The urgent need for autonomous navigation has promoted a popular topic of improving RINS accuracy. However, few have delved into error excitation and propagation from the perspective of carrier maneuvering. Starting from the error phenomena in a UAV flight experiment, this paper studies the error propagation mechanism of gyro installation error in dual-axis RINS under coordinated U-turn. Specifically, the coupling between the control strategies of the dual-axis RINS and the UAV typical maneuvers is revealed. On this basis, the error propagation mechanism for the gyro installation error is derived. Simulations validate the analyzed theory and illustrate the severity of this error source through flight and compensation experiments, where regular identification in highly dynamic applications becomes crucial to improve navigation accuracy.

A switched adaptive strategy for state estimation in MEMS-based inertial navigation systems with application for a flight test

Abstract This paper proposes a switched model to improve the estimation of Euler angles and decrease the inertial navigation system (INS) error, when the centrifugal acceleration occurs. Depending on the situation, one of the subsystems of the proposed switched model is activated for the estimation procedure. During global positioning system (GPS) outages, an extended Kalman filter (EKF) operates in the prediction mode and corrects the INS information, based on the system error model. Compared with previous works, the main advantages of the proposed switched-based adaptive EKF (SAEKF) method are (i) elimination of INS error, during the centrifugal acceleration, and (ii) high accuracy in estimating the attitude and positioning, particularly during GPS outages. To validate the efficiency of the proposed method in various trajectories, an experimental flight test is performed and discussed, involving a microelectromechanical (MEMS)-based INS. The comparative study shows that the proposed method considerably improves the accuracy in various scenarios.

Optimizing AUV Navigation Using Factor Graphs with Side-Scan Sonar Integration

For seabed mapping, the prevalence of autonomous underwater vehicles (AUVs) employing side-scan sonar (SSS) necessitates robust navigation solutions. However, the positioning errors of traditional strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) systems accumulated significantly, further exacerbated by DVL’s susceptibility to failure in complex underwater conditions. This research proposes an integrated navigation approach that utilizes factor graph optimization (FGO) along with an improved pre-integration technique integrating SSS-derived position measurements. Firstly, the reliability of SSS image registration in the presence of strong noise and feature-poor environments is improved by replacing the feature-based methods with a Fourier-based method. Moreover, the high-precision inertial measurement unit (IMU) pre-integration method could correct the heading errors of SINS significantly by considering the Earth’s rotation. Finally, the AUV’s marine experimental results demonstrated that the proposed integration method not only offers improved SSS image registration and corrects initial heading discrepancies but also delivers greater system stability, particularly in instances of DVL data loss.

Pedestrian Inertial Positioning Method Based on Foot Quasi-Zero Velocity Observation Under Multiple Motion Modes

As the traditional zero velocity update (ZUPT) algorithm based on fixed threshold is difficult to solve the problem of effective positioning for pedestrians under multiple motion modes, a simpler, flexible and stable inertial positioning algorithm is proposed in this paper. A foot kinematic model based on rotating “L" shaped rigid body is proposed to describe the process from heel landing to heel off the ground. The quasi-zero velocity interval (QZVI) of foot motion is defined, and a method to determine the QZVI based on peak detection and gait cycle constraint is proposed to avoid the dependence of system on predetermined threshold. The proposed foot kinematic model is used to calculate the “near zero" velocity in the QZVI. Then difference between the calculated “near zero" velocity and the inertial navigation solution velocity is used as the observation of Kalman filter to estimate the position error, velocity error and attitude error of inertial navigation system (INS). Experiments show that the proposed algorithm has positioning error relative to the mileage of less than 2% under the mileage of 500m or less, effectively adapting to different motion modes such as fast walking, slow walking and running, so as to realize the accurate autonomous positioning under the condition of multiple motion modes.

An effective fusion positioning methodology for land vehicles in GPS-denied environments using low-cost sensors

It is still a main challenge to achieve accurate and reliable vehicle positioning in Global Position System (GPS)-denied environments using low-cost sensors. Although existing methods have obtained a certain performance improvement, there is still room for further enhancement in positioning accuracy due to the lack of effective observation information during GPS outages. To address this challenge, this paper proposes an effective fusion positioning methodology based on enhanced observation information. Firstly, a data denoising algorithm based on convolutional denoising autoencoder is developed to effectively filter the noises in inertial sensors, so as to provide more clean data for subsequent modeling. Then, a differential fusion strategy is designed to selectively fuse multiple observation information from monocular camera, low-cost GPS and wheel speed sensor with extended Kalman filter, which can further improve the positioning accuracy. Finally, a long short-term memory-based error prediction model is constructed to learn the mapping relationship between the result of data denoising and differential fusion. In the case of GPS outages, the error model provides accurate corrections to compensate for position errors of inertial navigation system. The performance of the proposed methodology was evaluated on real-world data collected in complex urban environments. Experimental results indicate that the proposed positioning methodology can obtain significant accuracy enhancement.

Neural network model of errors of an autonomous aircraft strapdown inertial navigation system

Neural network model of errors of an autonomous aircraft strapdown inertial navigation system

ASBL: low-cost, small-sized AUV remote guidance method and experimental verification

The low-cost, small-sized autonomous underwater vehicle (AUV) is the preferred carrier for seabed operations. As all know, the inertial navigation system (INS) error drifts over time. Three or more seafloor datum signals are required when error-correcting the INS using the long baseline (LBL). Typically, a seafloor datum has an acoustic ranging capability of 15 km. However, the positioning ability of the conventional LBL is limited and cannot play the role of remote guidance when there is only a single datum. The paper proposes the Acoustic Synthetic Baseline (ASBL) positioning technology, which can achieve target positioning through multi-frame acoustic distance estimates between the measured target and the underwater datum. Firstly, the positioning principle of the ASBL is introduced, and the error distribution is analyzed in the paper. Secondly, a small-sized AUV is used to carry out the verification on the lake test, and the result shows that the ASBL positioning technology can play a guiding role. According to the statistics of the positioning results, the detection probability of less than 5% positioning error can reach more than 83%. The ASBL technology can reduce the dependence on the number of seafloor datums that convenience and operation efficiency can improve only by a single datum.

A Combination Scheme of Pure Strapdown and Dual-Axis Rotation Inertial Navigation Systems

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

Autonomous Cooperative Guidance Strategies for Unmanned Aerial Vehicles During On-Board Emergency

This paper presents autonomous guidance strategies for both solo and group of unmanned aerial vehicles (UAVs), to be used when emergency conditions are encountered during mission. Fixed-wing UAVs equipped with batteries that serve as on-board power source are considered. They are also equipped with inertial and supervisory satellite-based navigation system. An emergency condition where limited available on-board power along with nonavailability of both ground command link and satellite-based navigation information is considered. We proposed two guidance strategies, look-lock-land and inject-contribute-exit for single- and multiple-UAV missions. An optimization model is developed to arrive at the maximum area that can be covered by spiral trajectory without violating on-board power constraints. In addition, we also proved that spiral trajectory acts like a circular highway for multiple-UAV missions. Algorithms to achieve look-lock-land, inject-contribute-exit and to compute Inertial Navigation Systems (INS) error correction factor are discussed. The performance of the proposed autonomous emergency guidance strategies is demonstrated on a sea-based oil-well-monitoring mission.

Single-Axis Rotation Modulation Transverse SINS Based on Virtual Sphere Model in Polar Region

The measurement error of the inertial measurement unit (IMU) seriously affects the navigation accuracy of the strap-down inertial navigation system (SINS) in polar region. In order to improve navigation precision, a self-compensation method of the single-axis rotation modulation transverse SINS based on virtual sphere model is proposed in this paper. Based on the principle of single-axis rotation modulation and the mechanism of error propagation of transverse navigation system, the error characteristics of system errors were analyzed, such as constant bias, scale factor error, installation error and random walk. Finally, the simulation and experiment are designed to validate the performance of the proposed method. The navigation results demonstrate that the navigation errors of inertial navigation system, especially constant error, can be effectively restrained by the proposed single-axis rotation modulation transverse SINS based on virtual sphere model, enhancing accuracy in long-endurance navigation in polar region.

A New Self-Calibration and Compensation Method for Installation Errors of Uniaxial Rotation Module Inertial Navigation System.

Calibration and compensation techniques are essential to improve the accuracy of the strap-down inertial navigation system. Especially for the new uniaxial rotation module inertial navigation system (URMINS), replacing faulty uniaxial rotation modules introduces installation errors between modules and reduces navigation accuracy. Therefore, it is necessary to calibrate these systems effectively and compensate for the installation error between modules. This paper proposes a new self-calibration and compensation method for installation errors without additional information and equipment. Using the attitude, velocity, and position differences between the two sets of navigation information output from URMINS as measurements, a Kalman filter is constructed and the installation error is estimated. After URMINS is compensated for the installation error, the average of the demodulated redundant information is taken to calculate the carrier’s navigation information. The simulation results show that the proposed method can effectively assess the installation error between modules with an estimation accuracy better than 5”. Experimental results for static navigation show that the accuracy of heading angle and positioning can be improved by 73.12% and 81.19% after the URMINS has compensated for the estimated installation errors. Simulation and experimental results further validate the effectiveness of the proposed self-calibration and compensation method.

Research on Fast Self-alignment Method of SINS under Vehicle Jitter and Leveling Disturbance

Using the combination of simulation and actual measurement, analyze the impact of disturbances such as the jitter of the launch vehicle drive motor and the leveling motion of the launch vehicle on the output signals of the gyroscope and accelerometer, clarify the alignment error source of the SINS, and combine the error model to design based on the Kalman classical filter. For the algorithm’s variable mode filtering scheme, a variable mode filtering alignment method using the output pulse threshold of the horizontal gyro is proposed, and the filtering method in the fine alignment stage is improved. Vehicle experiments show that the proposed variable mode filtering method has a simple algorithm and a small amount of calculation, and can effectively suppress dynamic disturbances such as engine jitter and vehicle leveling motion, improve the convergence speed of the azimuth error of the strapdown inertial navigation system, and reduce the azimuth misalignment angle estimate bias.

Stereo-vision-based AUV navigation system for resetting the inertial navigation system error

Stereo-vision-based AUV navigation system for resetting the inertial navigation system error

Linear Track Underwater Carrier SINS Correction Method Based on Hydroacoustic Single Beacon

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

INS’s Error Compensation via Measurement Differencing Kalman Filter

The Inertial Navigation System (INS) error compensation for the case of time-correlated measurement errors is proposed in this study. Measurement differences are derived in the filter for the solution of the state estimation problem. In this case, the measurement noise for the derived measurements is no longer correlated in time, but it is correlated with the process noise. Therefore, in this study, the measurement differencing approach based Kalman filter (KF) is designed for the case of correlated system and measurement noise. The innovation properties of the proposed Measurement Differencing Kalman filter (MDKF) are investigated. Conventional KF, robust adaptive KF, and proposed MDKF were applied to estimate the states of a multi-input /output aircraft model in the presence of time-correlated INS measurements, and the obtained results were compared. It is shown that MDKF is also robust to the noise increment type INS errors.

Correction of the inertial navigation system using a genetic algorithm

The problem of compensating errors for an inertial navigation system with an unstable signal of a satellite navigation system is considered. The error model of the inertial navigation system, with the help of which its errors are predicted is built under the conditions of the signals disappearance from the satellite navigation system. The prediction of inertial navigation system errors are used for correction in the output signal. Predictive models are built using a genetic algorithm. Correction for small unmanned aerial vehicles, it is proposed to carry out using a scalar model of inertial navigation system errors and generated reduced measurements. Keywords aircraft, inertial navigation system, satellite navigation system, correction, genetic algorithm, reduced measurements

Complex mathematical models of inertial navigation system errors

The article is devoted to mathematical models of errors of inertial navigation systems (INS). The main advantages of autonomous inertial navigation systems are their resistance to horizontal accelerations and the ability to work autonomously under any conditions. However, over time, the errors of autonomous INS, due to the drift of gyroscopes, zero offset and drift of accelerometers, as well as other factors, reach significant values. Therefore, research to compensate for these errors is an important and urgent task in the autonomous mode of operation of the aircraft. To establish the connection between the output and input errors of autonomous INS, the equation of errors of autonomous INS is made. In this case, two models of errors of autonomous INS are investigated: nonlinear and linear. Depending on the requirement for accuracy and time of calculation of navigation parameters choose different models of INS errors. The linear model is simpler and requires less computational time. But the development of modern technologies allows to solve complex problems at an acceptable time interval. Therefore, it is possible to use nonlinear models.

Error Compensation of Strapdown Inertial Navigation System for the Boom-Type Roadheader under Complex Vibration

The strapdown inertial navigation system can provide the navigation information for the boom-type roadheader in the unmanned roadway tunneling working face of the coal mine. However, the complex vibration caused by the cutting process of the boom-type roadheader may result in significant errors of its attitude and position measured by the strapdown inertial navigation system. Thus, an error compensation method based on the vibration characteristics of the roadheader is proposed in this paper. In order to further analyze the angular and linear vibration of the fuselage, as the main vibration sources of the roadheader, the dynamic model of the roadheader is formulated based on the cutting load. Following that, multiple sub-samples compensation algorithms for the coning and sculling errors are constructed. Simulation experiments were carried out under different subsample compensation algorithms, different coal and rock characteristics, and different types of roadheader. The experimental results show that the proposed error compensation algorithm can eliminate the effect of the angular and linear vibration on the measurement accuracy. The coning and sculling error of the strapdown inertial navigation system can reduce at least 52.21% and 42.89%, respectively. Finally, a strapdown inertial navigation error compensation accuracy experiment system is built, and the validity and superiority of the method proposed in this paper are verified through calculation and analysis of the data collected on the actual tunneling work face.

High-speed train positioning method based on motion constraints suppressing INS error in tunnel

When the high-speed train is running in tunnel, the global navigation satellite system (GNSS) signal is completely lost, and only relying on the inertial navigation system (INS) composed of micro electro mechanical system (MEMS) devices makes large navigation error. To solve this problem, a method considering the installation angles of micro inertial measurement unit (MIMU) relative to the train body for motion constraints aided inertial navigation system (CIAMC-INS) is proposed, which does not need extra sensors. This method first establishes motion constraints model based on the installation angles of MIMU; secondly, the effect of turning on the motion constraints model is analyzed, and the use condition of motion constraints is obtained; then, the installation angles of MIMU are estimated when GNSS signal is good and the use condition of motion constraints is met; finally, the estimated installation angles are applied to the motion constraints to suppress the error of pure inertial navigation system (P-INS) to improve the navigation accuracy in tunnel. Based on this method, high-speed train navigation tests are carried out both in the real tunnel environment and in the case of artificially disconnected GNSS signal. The experimental results show that the navigation accuracy of the train in the tunnel is significantly improved, which verifies the effectiveness of the method.