Analysis of Gyro Bias Depending on the Position of Inertial Measurement Unit in Rotational Inertial Navigation Systems

Seo, Yoshiho; Yu, Haesung; Ryu, Kyungdon; Lee, In-Seop; Oh, Juhyun; Kim, Cheonjoong; Lee, Sang Jeong; Park, Chan-Sik

doi:10.3390/s22218355

Cited by 9 publications

(4 citation statements)

References 35 publications

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“…In the strapdown inertial navigation system, the commonly used coordinate systems include inertial frame, earth frame, geographic frame, navigation frame, body frame, etc. In the rotation inertial navigation system, another IMU frame is added [ 18 , 19 ]. The coordinate system used in the subsequent analysis of this paper is defined as follows:…”

Section: Coordinate System and Error Model Of Insmentioning

confidence: 99%

Design of a Multi-Position Alignment Scheme

Guan,

Liu,

Wei

et al. 2024

Sensors

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The current new type of inertial navigation system, including rotating inertial navigation systems and three-autonomy inertial navigation systems, has been increasingly widely applied. Benefited by the rotating mechanisms of these inertial navigation systems, alignment accuracy can be significantly enhanced by implementing IMU (Inertial Measurement Unit) rotation during the alignment process. The principle of suppressing initial alignment errors using rotational modulation technology was investigated, and the impact of various component error terms on alignment accuracy of IMU during rotation was analyzed. A corresponding error suppression scheme was designed to overcome the shortcoming of the significant scale factor error of fiber optic gyroscopes, and the research content of this paper is validated through corresponding simulations and experiments. The results indicate that the designed alignment scheme can effectively suppress the gyro scale factor error introduced by angular motion and improve alignment accuracy.

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Section: Coordinate System and Error Model Of Insmentioning

confidence: 99%

Design of a Multi-Position Alignment Scheme

Guan,

Liu,

Wei

et al. 2024

Sensors

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“…Rotational strapdown inertial attitude measurement technology has robust autonomy. It can weaken or compensate the influence of inertial sensor error without the usage of external information [ 15 , 16 ]. As shown in Fig 2 , The encoder installed on the rotating platform is embedded with the rotation configuration of SIAMS, which can measure the angle of IMU and the frame of the object to be measured.…”

Section: Backdrop and Devicesmentioning

confidence: 99%

“…This installation error will also cause attitude error. Eq (15) shows the gyroscope error caused by IMU installation error in the reciprocating rotation cycle.…”

Section: Plos Onementioning

confidence: 99%

MEMS strapdown inertial attitude measurement system using rotational modulation technology

Sun,

Huang,

Sun

et al. 2024

PLoS ONE

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Attitude determination involves the integration of methodologies and systems for estimating the time varying attitude of moving objects. Strapdown Inertial Attitude Measurement System (SIAMS) is among the most widely used navigation systems. The development of cost effective Micro Electro Mechanic System (MEMS) based inertial sensors has made attitude measurement system more affordable. However, MEMS sensors suffer from various errors that have to be calibrated and compensated to get acceptable attitude results. Given the auto-compensation of inertial sensor bias in rotation error modulation, the objective of this paper is to develop a MEMS-based rotary SIAMS, in which the significant sensor bias is automatically compensated by rotating the IMU, to offer comparable performance with respect to a tactical-grade Inertial Measurement Unit (IMU). With the analysis of the relationship between the MEMS error and misalignment, a MEMS calibration model is derived, and a combined calibration method of multi position rotation is applied to estimate the deterministic sensor errors such as bias, scale factor, and misalignment. Simulation and experiment results indicate that the proposed method can further modulate and compensate the MEMS errors, thereby improving the MEMS attitude accuracy.

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“…IMU's include gyroscopes, accelerometers and geomagnetism. These instruments are generally subject to certain installation errors, which can be greater under the influence of external factors, resulting in large deviations in the measurement process and causing inaccurate attitude information during steady state aiming [3].…”

Section: Introductionmentioning

confidence: 99%

Development of Steady Aiming System Based on Kalman Filter and Coordinate Transformation

Lu¹,

Liang

Tang

et al. 2023

IEEE Access

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The aiming accuracy of the Unmanned Aerial Vehicles (UAV) Steadicam head can be affected by many factors, such as the state of the UAV during the actual flight and the installation error of the system related hardware. In order to eliminate the influence of objective factors on the UAV Steadicam, a Kalman filter aiming algorithm based on the coordinate transformation method is proposed to eliminate the attitude error of the UAV Steadicam and improve the accuracy of the system. The algorithm uses coordinate transformation to eliminate mounting errors and combines coordinate transformation and Kalman filtering methods to eliminate objective errors of the UAV in flight. The experimental simulation results show that our method can accurately give the amount of azimuth and pitch angle error compensation during the flight of the UAV, improving the accuracy of the UAV Steadicam head. Ultimately, the method is applied to the development of a real product.INDEX TERMS Attitude error, coordinate transformation, error compensation, Kalman filtering, Steadicam head.

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Analysis of Gyro Bias Depending on the Position of Inertial Measurement Unit in Rotational Inertial Navigation Systems

Cited by 9 publications

References 35 publications

Design of a Multi-Position Alignment Scheme

Design of a Multi-Position Alignment Scheme

MEMS strapdown inertial attitude measurement system using rotational modulation technology

Development of Steady Aiming System Based on Kalman Filter and Coordinate Transformation

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