A blurred star image restoration method based on gyroscope data and enhanced sparse model

Yi, Jinhui; Ma, Yuebo; Zhao, Rujin; Zhu, Zifa; Zhu, Zijian; Tang, Yuping

doi:10.1088/1361-6501/ace730

Cited by 3 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, sparse representation (SR) theory, whose origins can be traced back to biological visual systems offering a flexible and effective tool for image denoising [24,25] and signal processing [26][27][28][29], has been widely praised for its outstanding performance. By utilizing only a small fraction of atoms from an overcomplete dictionary, SR can provide a more concise representation of the signal.…”

Section: Introductionmentioning

confidence: 99%

A fusion non-convex group sparsity difference method and its application in rolling bearing fault diagnosis

Wei,

Cai,

Liu

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Bearing fault is a common factor leading to machine failures. How to extract the periodic transient signal due to bearing faults submerged in strong noise is a challenging problem for bearing fault diagnosis. Total variation denoising is a method used to remove noise and extract features. However, it solely relies on the sparsity of the first-order difference of the signal, resulting in the loss of important features and underestimation of amplitude. Additionally, it fails to capture the periodicity of the signal. To address these issues, a novel approach called fusion non-convex group sparsity difference (FNC-GSD) method is proposed for bearing fault diagnosis. Firstly, it is recognized that noise does not exhibit sparsity in the difference domain, while transient signal exhibits group sparsity in the difference domain. This grouping property enhances the feature selection ability of sparse model. Inspired by this, the group sparsity of the transient signal in the difference domain is used to preserve the fault features as much as possible. Additionally, in order to promote sparsity of the signal itself in the time domain to preserve the potential impulse component, a l 1-norm regularization term is introduced. Furthermore, a non-convex sparsity-inducing penalty function strategy is employed to prevent amplitude underestimation. The proposed sparse model considers both the group sparsity in the difference domain and the sparsity in the time domain of the transient signal, and its solution is derived according to the majorization–minimization algorithm. And the particle swarm optimization algorithm is used to adaptively search the regularization parameters of FNC-GSD. Finally, multiple bearing fault diagnosis experiments are conducted to demonstrate the performance of the FNC-GSD. The results show that it has advantages in fault feature extraction compared with some other methods.

show abstract

Section: Introductionmentioning

confidence: 99%

A fusion non-convex group sparsity difference method and its application in rolling bearing fault diagnosis

Wei,

Cai,

Liu

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

On-orbit high-precision calibration for deep-coupled parameters of star sensor and gyroscope systems

Yi,

Ma,

Long

et al. 2024

Opt. Express

View full text Add to dashboard Cite

To enhance the dynamic performance and stability of spacecraft attitude measurement, a commonly adopted approach is to integrate a star sensor with an inertial gyroscope. While an integrated sensor system offers benefits in high-precision and high-dynamic scenarios, it also introduces errors in the extrinsic parameters of the camera and gyroscope, directly contributing to increased fixed error in attitude determination. Hence, real-time correction of integrated system parameters becomes crucial for enhancing the accuracy of attitude estimation. However, a deep coupling relationship exists between the camera's principal point and the extrinsic parameters of the two sensors, unified calibration of system parameters increases the computational time to decouple all the parameters. In response to this challenge, a stepwise calibration method for system parameters is proposed. Firstly, the calibration of camera intrinsic parameters leverages the principle of invariant interior star angles, which are independent of extrinsic parameters. Secondly, based on the observation vector error model of the integrated sensor, observation equations and state equations are constructed, followed by proposing a 4-position maneuver strategy based on observability analysis to achieve rapid decoupling of gyroscope bias, attitude error, and extrinsic parameters. Simulation results affirm the effectiveness of the proposed method, extrinsic parameter errors reaching the arc-second level.

show abstract

Tightly Coupled Visual–Inertial Fusion for Attitude Estimation of Spacecraft

Yi,

Ma,

Long

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

The star sensor boasts the highest accuracy in spacecraft attitude measurement. However, it is vulnerable to disturbances, including high-dynamic motion, stray light, and various in-orbit environmental factors. These disruptions may lead to a significant decline in attitude accuracy or even abnormal output, potentially inducing a state of disorientation in the spacecraft. Thus, it is usually coupled with a high-frequency gyroscope to compensate for this limitation. Nevertheless, the accuracy of long-term attitude estimation using a gyroscope decreases due to the presence of bias. We propose an optimization-based tightly coupled scheme to enhance attitude estimation accuracy under dynamic conditions as well as to bolster the star sensor’s robustness in cases like lost-in-space. Our approach commenced with visual–inertial measurement preprocessing and estimator initialization. Subsequently, the enhancement of attitude and bias estimation precision was achieved by minimizing visual and inertial constraints. Additionally, a keyframe-based sliding window approach was employed to mitigate potential failures in visual sensor measurements. Numerical tests were performed to validate that, under identical dynamic conditions, the proposed method achieves a 50% improvement in the accuracy of yaw, pitch, and roll angles in comparison to the star sensor only.

show abstract

A blurred star image restoration method based on gyroscope data and enhanced sparse model

Cited by 3 publications

References 33 publications

A fusion non-convex group sparsity difference method and its application in rolling bearing fault diagnosis

A fusion non-convex group sparsity difference method and its application in rolling bearing fault diagnosis

On-orbit high-precision calibration for deep-coupled parameters of star sensor and gyroscope systems

Tightly Coupled Visual–Inertial Fusion for Attitude Estimation of Spacecraft

Contact Info

Product

Resources

About