An Improved System-Level Calibration Scheme for Rotational Inertial Navigation Systems

Wei, Qiushuo; Zha, Feng; He, Hongyang; Bao, Li

doi:10.3390/s22197610

Cited by 8 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

MEMS strapdown inertial attitude measurement system using rotational modulation technology

Sun,

Huang,

Sun

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

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.

show abstract

Section: Backdrop and Devicesmentioning

confidence: 99%

“…Eq (16) describes the gyroscope error caused by the installation error, and Eq (17) represents the whole circle rotation integration under the navigation frame [23].…”

Section: Plos Onementioning

confidence: 99%

MEMS strapdown inertial attitude measurement system using rotational modulation technology

Sun,

Huang,

Sun

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…RINSs can be conveniently self-calibrated without an external turntable by exciting error parameters through the rotation of their own gimbal mechanisms. A 30-position calibration scheme was proposed for a dual-axis RINS in reference [ 14 ], which could calibrate 21 IMU errors by periodically rotating the axis.…”

Section: Introductionmentioning

confidence: 99%

A Fast Self-Calibration Method for Dual-Axis Rotational Inertial Navigation Systems Based on Invariant Errors

Sun,

Lai,

Lyu

et al. 2024

Sensors

View full text Add to dashboard Cite

In order to ensure that dual-axis rotational inertial navigation systems (RINSs) maintain a high level of accuracy over the long term, there is a demand for periodic calibration during their service life. Traditional calibration methods for inertial measurement units (IMUs) involve removing the IMU from the equipment, which is a laborious and time-consuming process. Reinstalling the IMU after calibration may introduce new installation errors. This paper focuses on dual-axis rotational inertial navigation systems and presents a system-level self-calibration method based on invariant errors, enabling high-precision automated calibration without the need for equipment disassembly. First, navigation parameter errors in the inertial frame are expressed as invariant errors. This allows the corresponding error models to estimate initial attitude even more rapidly and accurately in cases of extreme misalignment, eliminating the need for coarse alignment. Next, by utilizing the output of a gimbal mechanism, angular velocity constraint equations are established, and the backtracking navigation is introduced to reuse sensor data, thereby reducing the calibration time. Finally, a rotation scheme for the IMU is designed to ensure that all errors are observable. The observability of the system is analyzed based on a piecewise constant system method and singular value decomposition (SVD) observability analysis. The simulation and experimental results demonstrate that this method can effectively estimate IMU errors and installation errors related to the rotation axis within 12 min, and the estimated error is less than 4%. After using this method to compensate for the calibration error, the velocity and position accuracies of a RINS are significantly improved.

show abstract

“…Navigation system refers to the general term of all equipment combinations that can complete certain navigation and positioning tasks. At present, the main navigation systems include radio navigation system [1,2], satellite navigation system [3][4][5], astronomical navigation system [6], inertial navigation system [7,8] and integrated navigation system [9][10][11], etc.…”

Section: Introductionmentioning

confidence: 99%

An GNSS/INS Integrated Navigation Algorithm Based on PSO-LSTM in Satellite Rejection

et al. 2023

View full text Add to dashboard Cite

When the satellite signal is lost or interfered with, the traditional GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) integrated navigation will degenerate into INS, which results in the decrease in navigation accuracy. To solve these problems, this paper mainly established the PSO (particle swarm optimization) -LSTM (Long Short-Term Memory) neural network model to predict the increment of GNSS position under the condition of satellite rejection and accumulation to obtain the pseudo-GNSS signal. The signal is used to compensate for the observed value in the integrated system. The model takes the advantages of LSTM, which is good at processing time series, and uses PSO to obtain the optimal value of important hyperparameters efficiently. Meanwhile, the improved threshold function is used to denoise the IMU (inertial measurement unit) data, which improves the SNR (signal-to-noise ratio) of IMU outputs effectively. Finally, the performance of the algorithm is proved by actual road test. Compared with INS, the method can reduce the maximum errors of latitude and longitude by at least 98.78% and 99.10% while the satellite is lost for 60 s, effectively improving the accuracy of the GNSS/INS system in satellite rejection.

show abstract

An Improved System-Level Calibration Scheme for Rotational Inertial Navigation Systems

Cited by 8 publications

References 13 publications

MEMS strapdown inertial attitude measurement system using rotational modulation technology

MEMS strapdown inertial attitude measurement system using rotational modulation technology

A Fast Self-Calibration Method for Dual-Axis Rotational Inertial Navigation Systems Based on Invariant Errors

An GNSS/INS Integrated Navigation Algorithm Based on PSO-LSTM in Satellite Rejection

Contact Info

Product

Resources

About