Systematic error analysis and compensation for high accuracy star centroid estimation of star tracker

Jia, Hui; Yang, Jiankun; Li, Xiujian; Yang, Juncai; Yang, Ming; Liu, YiWu; Hao, Yue

doi:10.1007/s11431-010-4129-7

Cited by 30 publications

(16 citation statements)

References 9 publications

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“…This is because the I k / I 0 is decreasing and the energy center of each pixel becomes nearer to the geometric center with the lengthening of the star-spot trail. The amplitude of the systematic error in the static condition is exp[−2(πσ) 2 ]/π, which mainly depends on the Gaussian radius [9]. For the 0.7 pixel Gaussian radius, it is just about 2 × 10 −5 pixels which is in agreement with the above simulation.…”

Section: Star Location Accuracysupporting

confidence: 81%

Exposure Time Optimization for Highly Dynamic Star Trackers

Wei

Tan

et al. 2014

Sensors

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Under highly dynamic conditions, the star-spots on the image sensor of a star tracker move across many pixels during the exposure time, which will reduce star detection sensitivity and increase star location errors. However, this kind of effect can be compensated well by setting an appropriate exposure time. This paper focuses on how exposure time affects the star tracker under highly dynamic conditions and how to determine the most appropriate exposure time for this case. Firstly, the effect of exposure time on star detection sensitivity is analyzed by establishing the dynamic star-spot imaging model. Then the star location error is deduced based on the error analysis of the sub-pixel centroiding algorithm. Combining these analyses, the effect of exposure time on attitude accuracy is finally determined. Some simulations are carried out to validate these effects, and the results show that there are different optimal exposure times for different angular velocities of a star tracker with a given configuration. In addition, the results of night sky experiments using a real star tracker agree with the simulation results. The summarized regularities in this paper should prove helpful in the system design and dynamic performance evaluation of the highly dynamic star trackers.

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Section: Star Location Accuracysupporting

confidence: 81%

Exposure Time Optimization for Highly Dynamic Star Trackers

Wei

Tan

et al. 2014

Sensors

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“…The key to compensating systematic error in the CoM method lies in predicting the corresponding systematic error σx g of the actual star centroid positionx g accurately and rapidly. Researchers have proposed several compensation methods in recent years, such as the BP method [16], analytical compensation (AC) method [17], bivariate polynomial method [18], and LSSVR compensation method [19], and they can reduce the systematic error to some extent, but there are some shortcomings in the compensation model of these methods. For example, the poor performance indices and low learning rate of the BP algorithm, along with how easily it becomes trapped in a local optimum, limit the compensation accuracy of this method; the simplified approximation and iteration estimation in the analytical compensation method lead to a reduction in the prediction accuracy and high on-line computational complexity, which is not suitable for the on-orbit embedded system; the bivariate polynomial compensation template is valid only for some specific Gaussian width cases, and its application range is thus limited; and the problem of scientifically setting the penalty factor and kernel parameter in LSSVR still remains unsettled, meaning that model training is more difficult because of a time-consuming parameter selection process.…”

Section: Methodsmentioning

confidence: 99%

A Systematic Error Compensation Method Based on an Optimized Extreme Learning Machine for Star Sensor Image Centroid Estimation

et al. 2019

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As an important error in star centroid location estimation, the systematic error greatly restricts the accuracy of the three-axis attitude supplied by a star sensor. In this paper, an analytical study about the behavior of the systematic error in the center of mass (CoM) centroid estimation method under different Gaussian widths of starlight energy distribution is presented by means of frequency field analysis and numerical simulations. Subsequently, an optimized extreme learning machine (ELM) based on the bat algorithm (BA) is adopted to predict the systematic error of the actual star centroid position and then compensate the systematic error from the CoM method. In the BA-ELM model, the input weights matrix and hidden layer biases parameters are encoded as microbat’s locations and optimized by utilizing the strong global search capacity of BA, which significantly improves the performance of ELM in terms of prediction accuracy. The simulation result indicates that our method can reduce the systematic error to less than 3.0 × 10−7 pixels, and its compensation accuracy is two or three orders of magnitude higher than that of other methods for estimating a star centroid location under a 3 × 3 pixel sampling window.

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“…Subpixel peak localization methods have been widely studied not only to locate point sources, but also implement high accuracy stars trackers systems [8]. In this section, we present three different spot localization methods to be used sensing module: Centroid, centroid windowed and centroid squared.…”

Section: Spot Localization Methodsmentioning

confidence: 99%

Novel non-mechanical fine tracking module for retroreflective free space optics

Quintana

Gómez

Faulkner

et al. 2014

Unmanned/Unattended Sensors and Sensor Networks X

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The new generation of UAVs (Unmanned Aerial Vehicles) require high speed data links to offload all its sensors data. RFSO (Reflective Free Space Optics) has become an important alternative to RF systems because it is robust against interception and jamming, enhancing data security. Moreover, the weight and power consumption of the RFSO coms module is reduced, making it suitable for SWaP (Size, Weight, and Power) constrained applications. In this paper, we present the design of a tracking module based on a non-mechanical holographic beam steering system. A highly accurate position sensing unit is required to accomplish a good tracking process and therefore guarantee the data link stability. Different localization methods such as centroid, centroid windowed or centroid squared are tested and compared using real data captured in a turbulent scenario. Errors below 8cm are reported in a double pass 1km link.

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Systematic error analysis and compensation for high accuracy star centroid estimation of star tracker

Cited by 30 publications

References 9 publications

Exposure Time Optimization for Highly Dynamic Star Trackers

Exposure Time Optimization for Highly Dynamic Star Trackers

A Systematic Error Compensation Method Based on an Optimized Extreme Learning Machine for Star Sensor Image Centroid Estimation

Novel non-mechanical fine tracking module for retroreflective free space optics

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