Study on Integration of FOG Single-Axis Rotational INS and Odometer for Land Vehicle

Gao, Jiaxin; Li, Kui; Chen, Yuanpei

doi:10.1109/jsen.2017.2772308

Cited by 33 publications

(13 citation statements)

References 15 publications

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“…The navigation error propagation equations of SINS have been studied many times [18][19][20][21]. So, the error equations in n-frame of the SINS could be expressed as (15): where a ij (i/j = 1, 2, 3) represents the element in the transformation matrix C n b separately. In n-frame, δv n is the velocity error vector.…”

Section: 3 Sins Error Modelmentioning

confidence: 99%

“…where F is the system transformation matrix, which could be obtained according to (15). G represents the transformation matrix of the system noise.…”

Section: State Equation Of the Systemmentioning

confidence: 99%

“…It adopted the rotation modulation technique to suppress the inertial sensor errors, especially z-axis FOG drift; at the same time, it combined the FRINS with odometer to suppress the divergence of INS error over time. The experiments showed that the positioning accuracy of the vehicle was effectively enhanced, and the cost of the system was saved [15].…”

Section: Introductionmentioning

confidence: 99%

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Research on the Integrated Navigation Technology of SINS with Couple Odometers for Land Vehicles

Gao

2020

Sensors

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Autonomous and accurate acquisition of the position and azimuth of the vehicle is critical to the combat effectiveness of land-fighting vehicles. The integrated navigation system, consisting of a strap-down inertial navigation system (SINS) and odometer (OD), is commonly applied in vehicles. In the SINS/OD integrated system, the odometer is installed around the vehicle’s wheel, while SINS is usually installed on the base of the vehicle. The distance along SINS and OD would cause a velocity difference when the vehicle maneuvers, which may lead to a significant influence on the integration positioning accuracy. Furthermore, SINS navigation errors, especially azimuth error, would diverge over time due to gyro drifts and accelerometer biases. The azimuth error would cause the divergence of dead-reckoning positioning errors with the distance that the vehicle drives. To solve these problems, an integrated positioning and orientation method based on the configuration of SINS and couple odometers was proposed in this paper. The proposed method designed a high precision integrated navigation algorithm, which compensated the lever arm effect to eliminate the velocity difference between SINS and odometers. At the same time, by using the measured information of couple odometers, azimuth reference was calculated and used as an external measurement to suppress SINS azimuth error’s divergence over time, thus could further improve the navigation precision of the integrated system, especially the orientation accuracy. The performance of the proposed method was verified by simulations. The results demonstrated that SINS/2ODs integrated system could achieve a positioning accuracy of 0.01% D (total mileage) and orientation accuracy of ±30″ by using SINS with 0.01°/h Fiber-Optic Gyroscope (FOGs) and 50 µg accelerometers.

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Section: 3 Sins Error Modelmentioning

confidence: 99%

“…where F is the system transformation matrix, which could be obtained according to (15). G represents the transformation matrix of the system noise.…”

Section: State Equation Of the Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on the Integrated Navigation Technology of SINS with Couple Odometers for Land Vehicles

Gao

2020

Sensors

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“…For example, the RM technique was applied to enhance the performance of the INS in an autonomous underwater vehicle [3]. A fiber optic gyros (FOG) single-axis RINS was combined with an odometer to improve the positioning and orientation accuracy of a land vehicle [4]. To further improve the performance of the RINS, an improved 8-position rotation scheme and a novel 16-position rotation scheme were proposed for the optical gyro-based RINS [1].…”

Section: Introductionmentioning

confidence: 99%

A System-Level Self-Calibration Method for Installation Errors in A Dual-Axis Rotational Inertial Navigation System

Bai

Lai

Lyu

et al. 2019

Sensors

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In a dual-axis rotational inertial navigation system (RINS), there are two kinds of installation errors, nonorthogonal installation errors of inertial sensors, and installation errors between the inertial measurement unit (IMU) and rotation axes. Traditionally, these two errors are not considered simultaneously. Thus, they are calibrated separately by different estimation algorithms and rotation schemes. In this paper, a system-level self-calibration method for installation errors of a dual-axis RINS is proposed. Based on the Kalman filter, the measurement model is reestablished to ensure that all installation errors can be estimated together. First, the relationship between the initial attitude and subsequent attitude of IMU during rotation is used as a constraint to estimate nonorthogonal installation errors of accelerometers, and installation errors between the IMU and rotation axes. Then, the angular rate of the rotation mechanism is used as a reference to estimate nonorthogonal installation errors of the gyros. The rotation scheme of the IMU is designed to make all installation errors observable, and the observability of the system is analyzed based on the piecewise constant system method. Simulation and laboratory experiment results suggest that installation errors can be effectively estimated by the proposed method, thereby avoiding the complex separating process.

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“…Since slight odometer scale factor error can induce large navigation error, it is important to accurately estimate it. Previous works [3], [38]- [40] estimated the odometer scale factor in INS/GPS/odometer system which will be described in section II-A. However, these researches related to INS/GPS/odometer are also based on INS/GPS, which uses acceleration integration for obtaining velocity, so that it causes larger velocity and position error than DR/GPS which uses velocity information directly without integration.…”

Section: Introductionmentioning

confidence: 99%

MEMS 3D DR/GPS Integrated System for Land Vehicle Application Robust to GPS Outages

et al. 2019

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In this paper, we propose a MEMS 3D DR/GPS integrated system which provides 3D attitude and position information for land vehicle application. Since low-cost MEMS-IMU provides data with large bias, it is hard to obtain an accurate position of the land vehicle, especially during GPS outages. To improve the performance of the MEMS-based navigation system, odometer-based 3D DR/GPS formulation is proposed. The proposed algorithm uses 1D speed information for obtaining 3D position and extended Kalman filter (EKF) to compensate for navigation errors. An error model for the EKF is developed, including the odometer scale factor, which may change over various environments and require an accurate value. By substituting a MEMS INS/GPS system to 3D DR/GPS system, the velocity and position errors of navigation solution are reduced. Since the proposed system estimates 3D attitude and position unlike conventional DR/GPS system, it suppresses navigation errors effectively when a land vehicle goes uphill and downhill even for the GPS outages. Besides, the adaptation rule for measurement noise covariance is applied to improve navigation performance. The proposed method is tested for a land vehicle in an urban area, including GPS outages. The experimental results show that the proposed MEMS 3D DR/GPS system provides a more stable navigation solution with fewer position errors.

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Study on Integration of FOG Single-Axis Rotational INS and Odometer for Land Vehicle

Cited by 33 publications

References 15 publications

Research on the Integrated Navigation Technology of SINS with Couple Odometers for Land Vehicles

Research on the Integrated Navigation Technology of SINS with Couple Odometers for Land Vehicles

A System-Level Self-Calibration Method for Installation Errors in A Dual-Axis Rotational Inertial Navigation System

MEMS 3D DR/GPS Integrated System for Land Vehicle Application Robust to GPS Outages

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