A star identification algorithm for rolling shutter exposure based on Hough transform

The star identification algorithm usually identifies stars by angular distance matching.However,under high dynamic conditions,the rolling shutter effect distorts the angular distances between the measured and true star positions,leading to plethoric false matches and requiring complex and time-consuming verification for star identification.Low identification rate hinders the application of low-noise and cost-effective rolling shutter image sensors.In this work,we first study a rolling shutter distortion model of angular distances between stars,and then propose a novel three-stage star identification algorithm to identify distorted star images captured by the rolling shutter star sensor.The first stage uses a modified grid algorithm with adaptive error tolerance and an expanded pattern database to efficiently eliminate spurious matches.The second stage performs angular velocity estimation based on Hough transform to verify the matches that follow the same distortion pattern.The third stage applies a rolling shutter error correction method for further verification.Both the simulation and night sky image test demonstrate the effectiveness and efficiency of our algorithm under high dynamic conditions.The accuracy of angular velocity estimation method by Hough transform is evaluated and the root mean square error is below 0.5(°)/s.Our algorithm achieves a 95.7% identification rate at an angular velocity of 10(°)/s,which is much higher than traditional algorithms.

Precession–nutation correction for star tracker attitude measurement of STECE satellite

Space Technology Experiment and Climate Exploration （STECE） is a small satellite mis- sion of China for space technology experiment and climate exploration. A new test star tracker and one ASTRO 10 star tracker have been loaded on the STECE satellite to test the new star tracker＇s measurement performance. However, there is no autonomous precession nutation correction func- tion for the test star tracker, which causes an apparent periodic deflection in the inter-boresight angle between the two star trackers with respect to each other of up to - 500 arcsec, so the preces- sion and nutation effect needs to be considered while assessing the test star tracker. This paper researches on the precession-nutation correction for the test star traeker＇s attitude measurement and presents a precession-nutation correction method based on attitude quaternion data. The peri- odic deflection of the inter-boresight angle between the two star trackers has been greatly eliminated after the precession and nutation of the test star tracker＇s attitude data have been corrected by the proposed method and the validity of the proposed algorithm has been demonstrated. The in-flight accuracy of the test star tracker has been assessed like attitude noise and low-frequency errors after the precession-nutation correction.

Low-frequency Periodic Error Identification and Compensation for Star Tracker Attitude Measurement

The low-frequency periodic error of star tracker is one of the most critical problems for high-accuracy satellite attitude determination.In this paper an approach is proposed to identify and compensate the low-frequency periodic error for star tracker in attitude measurement.The analytical expression between the estimated gyro drift and the low-frequency periodic error of star tracker is derived firstly.And then the low-frequency periodic error,which can be expressed by Fourier series,is identified by the frequency spectrum of the estimated gyro drift according to the solution of the first step.Furthermore,the compensated model of the low-frequency periodic error is established based on the identified parameters to improve the attitude determination accuracy.Finally,promising simulated experimental results demonstrate the validity and effectiveness of the proposed method.The periodic error for attitude determination is eliminated basically and the estimation precision is improved greatly.

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

Subpixel centroid estimation is the most important star image location method of star tracker. This paper presents a theoretical analysis of the systematic error of subpixel centroid estimation algorithm utilizing frequency domain analysis under the con-sideration of sampling frequency limitation and sampling window limitation. Explicit expression of systematic error of cen-troid estimation is obtained, and the dependence of systematic error on Gaussian width of star image, actual star centroid loca-tion and the number of sampling pixels is derived. A systematic error compensation algorithm for star centroid estimation is proposed based on the result of theoretical analysis. Simulation results show that after compensation, the residual systematic errors of 3-pixel-and 5-pixel-windows’ centroid estimation are less than 2×10-3 pixels and 2×10-4 pixels respectively.

An artificial intelligence enhanced star identification algorithm

An artificial intelligence enhanced star identification algorithm is proposed for star trackers in lost-inspace mode.A convolutional neural network model based on Vgg16 is used in the artificial intelligence algorithm to classify star images.The training dataset is constructed to achieve the networks’optimal performance.Simulation results show that the proposed algorithm is highly robust to many kinds of noise,including position noise,magnitude noise,false stars,and the tracker’s angular velocity.With a deep convolutional neural network,the identification accuracy is maintained at 96%despite noise and interruptions,which is a significant improvement to traditional pyramid and grid algorithms.

Brightness Independent 4-Star Matching Algorithm for Lost-in-Space 3-Axis Attitude Acquisition

A star identification algorithm was developed for a charge-coupled device （CCD） or complementary metal-oxide-semiconductor （CMOS） autonomous star tracker to acquire 3-axis attitude information for a lost-in-space spacecraft. The algorithm took advantage of an efficient on-board database and an original “4- star matching” pattern recognition strategy to achieve fast and reliable star identification. The on-board database was composed of a brightness independent guide star catalog （mission catalog） and a K-vector star pair catalog. The star pattern recognition method involved direct location of star pair candidates and a sim- ple array matching procedure. Tests of the algorithm with a CMOS active pixel sensor （APS） star tracker result in a 99.9% success rate for star identification for lost-in-space 3-axis attitude acquisition when the angular measurement accuracy of the star tracker is at least 0.01°. The brightness independent algorithm requires relatively higher measurement accuracy of the star apparent positions that can be easily achieved by CCD or CMOS sensors along with subpixel centroiding techniques.

Optimization method for star tracker orientation in the sun-pointing mode

The star tracker is an optical attitude sensor with extremely high accuracy, the structure of which is mainly composed of the baffle, the lens, the image detector, and the processing unit, and it has been widely used in satel- lites To realize attitude determination from the weak star captured by the image detector of the star tracker,