The Tobit-Unscented-Kalman-Filter-Based Attitude Estimation Algorithm Using the Star Sensor and Inertial Gyro Combination

Wang, Xinmei; Zhang, Hui; Gao, Xiaodong; Zhao, Rujin

doi:10.3390/mi14061243

Cited by 3 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because the monorail crane is a highly nonlinear system, both the driving state and outside noise can significantly affect how accurately the track slope can be recognized when traveling. Compared with the EKF algorithm, UKF can effectively filter out the interference of disturbance variables, improve the anti-interference ability of the system, and realize the precise identification of the monorail track slopes [31].…”

Section: Ukf Algorithmmentioning

confidence: 99%

DFFRLS-FAUKF: accurate and reliable monorail longitudinal slope identification method

Liu,

Li,

Zheng

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Monorail cranes have always played an important role in mine auxiliary transportation systems owing to their excellent transportation performance and are therefore a desirable area in which to apply driverless technologies. However, the low-accuracy recognition of monorail track slopes and the poor reliability of recognition results make it difficult and dangerous to implement fully driverless monorail cranes. Aiming to solve these problems, a method for the accurate identification of longitudinal monorail slopes based on the use of a dynamic forgetting factor for recursive least squares (DFFRLS) and a fuzzy adaptive unscented Kalman filter (FAUKF) is proposed. First, acquired acceleration and velocity data are pre-processed using a rolling window. Second, the real-time longitudinal track-curvature value is calculated using the DFFRLS algorithm with the processed data and an established track-curvature model. Finally, based on existing track-curvature values, dynamic recognition of the monorail track slope is realised using the FAUKF algorithm with a fuzzy control factor, improving the accuracy of track gradient recognition. Experiments show that the DFFRLS-FAUKF algorithm improves the accuracy of track-slope recognition by up to 21.26% and 33.93% on average compared with that of DFFRLS with an adaptive extended Kalman filter (DFFRLS-AEKF) or an adaptive unscented Kalman filter (DFFRLS-AUKF).

show abstract

Section: Ukf Algorithmmentioning

confidence: 99%

DFFRLS-FAUKF: accurate and reliable monorail longitudinal slope identification method

Liu,

Li,

Zheng

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…This approach contributes to enhancing the precision of the entire navigation system. When passing through a tunnel, although the

signal is blocked and the

information is inaccurate, the attitude information in the TMR digital compass is accurate and can continue to provide attitude correction for the

[ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ].…”

Section: Design Of High-precision Portable Digital Compass Systemmentioning

confidence: 99%

Development and Application of a High-Precision Portable Digital Compass System for Improving Combined Navigation Performance

Zhang,

Cui,

Zhang

2024

Sensors

View full text Add to dashboard Cite

There are not many high-precision, portable digital compass solutions available right now that can enhance combined navigation systems’ overall functionality. Additionally, there is a dearth of writing about these products. This is why a tunnel magnetoresistance (TMR) sensor-based high-precision portable digital compass system is designed. First, the least-squares method is used to compensate for compass inaccuracy once the ellipsoid fitting method has corrected manufacturing and installation errors in the digital compass system. Second, the digital compass’s direction angle data is utilized to offset the combined navigation system’s mistake. The final objective is to create a high-performing portable TMR digital compass system that will enhance the accuracy and stability of the combined navigation system (abbreviated as CNS). According to the experimental results, the digital compass’s azimuth accuracy was 4.1824° before error compensation and 0.4580° after it was applied. The combined navigation system’s path is now more accurate overall and is closer to the reference route than it was before the digital compass was added. Furthermore, compared to the combined navigation route without the digital compass, the combined navigation route with the digital compass included is more stable while traveling through the tunnel. It is evident that the digital compass system’s design can raise the integrated navigation system’s accuracy and stability. The integrated navigation system’s overall performance may be somewhat enhanced by this approach.

show abstract

“…However, the attitude update rate (AUR) of the traditional star sensor is usually 4–10 Hz, which is too low to accomplish navigation tasks independently. In order to improve the AUR, an integrated navigation system of a star sensor and inertial measurement unit (IMU) has become a kind of classic solution [ 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. The attitude data of the star sensor have no drift but the update rate is low, while the update rate of the IMU is high, but the attitude data drift with time.…”

Section: Introductionmentioning

confidence: 99%

A High-Accuracy Star Centroid Extraction Method Based on Kalman Filter for Multi-Exposure Imaging Star Sensors

Yu,

Qu,

Zhang

2023

Sensors

View full text Add to dashboard Cite

A multi-exposure imaging approach proposed in earlier studies is used to increase star sensors’ attitude update rate by N times. Unfortunately, serious noises are also introduced in the star image due to multiple exposures. Therefore, a star centroid extraction method based on Kalman Filter is proposed in this paper. Firstly, star point prediction windows are generated based on centroids’ kinematic model. Secondly, the classic centroid method is used to calculate the coarse centroids of the star points within the prediction windows. Lastly, the coarse centroids are, respectively, processed by each Kalman Filter to filter image noises, and thus fine centroids are obtained. Simulations are conducted to verify the Kalman-Filter-based estimation model. Under noises with zero mean and ±0.4, ±1.0, and ±2.5 pixel maximum deviations, the coordinate errors after filtering are reduced to about 37.5%, 26.3%, and 20.7% of the original ones, respectively. In addition, experiments are conducted to verify the star point prediction windows. Among 100 star images, the average proportion of the number of effective star point objects obtained by the star point prediction windows in the total object number of each star image is calculated as only 0.95%. Both the simulated and experimental results demonstrate the feasibility and effectiveness of the proposed method.

show abstract

The Tobit-Unscented-Kalman-Filter-Based Attitude Estimation Algorithm Using the Star Sensor and Inertial Gyro Combination

Cited by 3 publications

References 22 publications

DFFRLS-FAUKF: accurate and reliable monorail longitudinal slope identification method

DFFRLS-FAUKF: accurate and reliable monorail longitudinal slope identification method

Development and Application of a High-Precision Portable Digital Compass System for Improving Combined Navigation Performance

A High-Accuracy Star Centroid Extraction Method Based on Kalman Filter for Multi-Exposure Imaging Star Sensors

Contact Info

Product

Resources

About