Systematic centroid error compensation for the simple Gaussian PSF in an electronic star map simulator

Wang, Yonghai; Li, Zhifeng; Song, Zhenfei

doi:10.1016/j.cja.2014.03.027

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…The luminance distribution of a star in the star image depends on the point spread function (PSF) and the position of the star. In common with many previous studies, [8][9][10][11] 2D Gaussian PSF is used. In order to obtain the simulating verisimilitude about the earth observing sensors, the gray diffusion also needs to be done with each of the star image spots.…”

Section: Image Simulationmentioning

confidence: 99%

Star centroid extraction in the image sequences of a discrete detector array on geostationary orbit

Qiao

Han²,

Zhang³

2022

Image and Signal Processing for Remote Sensing XXVIII

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Satellite image positioning and registration is a key technology for geostationary Earth observation satellites. Our work can provide powerful prior information for the positioning and registration of Earth observation images. We simulate the process of geostationary orbit satellites observing the space region with discrete detector array, generate star observation sequence images considering various situation, and study the high precision subpixel star centroid extraction algorithm. Under the condition that the star trajectory passes through the pixel, the proposed algorithm achieves the extraction error of less than 0.5 pixels.

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Section: Image Simulationmentioning

confidence: 99%

Star centroid extraction in the image sequences of a discrete detector array on geostationary orbit

Qiao

Han²,

Zhang³

2022

Image and Signal Processing for Remote Sensing XXVIII

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show abstract

“…During the experiment, the sampling window was fixed at 3 × 3 pixels, and the ideal point spread function (IPSF) model [23] was utilized to simulate a space discretized digital gray star image spot, that is, the signal intensity detected by the ith pixel was…”

Section: Discrete Approximation Errormentioning

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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“…With the development of the aerospace industry, the demand for high-precision and real-time star sensors is increasing day by day [4]. As the dynamic star simulator is the ground-test calibration equipment for star-sensitive instruments, the improvement of its star map display speed can improve the efficiency of star map identification by star sensors.…”

Section: Introductionmentioning

confidence: 99%

Field Programmable Gate Array-Based Acceleration Algorithm Design for Dynamic Star Map Parallel Computing

Cui,

Wang,

et al. 2024

Algorithms

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The dynamic star simulator is a commonly used ground-test calibration device for star sensors. For the problems of slow calculation speed, low integration, and high power consumption in the traditional star chart simulation method, this paper designs a FPGA-based star chart display algorithm for a dynamic star simulator. The design adopts the USB 2.0 protocol to obtain the attitude data, uses the SDRAM to cache the attitude data and video stream, extracts the effective navigation star points by searching the starry sky equidistant right ascension and declination partitions, and realizes the pipelined displaying of the star map by using the parallel computing capability of the FPGA. Test results show that under the conditions of chart field of view of Φ20° and simulated magnitude of 2.0∼6.0 Mv, the longest time for calculating a chart is 72 μs under the clock of 148.5 MHz, which effectively improves the chart display speed of the dynamic star simulator. The FPGA-based star map display algorithm gets rid of the dependence of the existing algorithm on the computer, reduces the volume and power consumption of the dynamic star simulator, and realizes the miniaturization and portable demand of the dynamic star simulator.

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Systematic centroid error compensation for the simple Gaussian PSF in an electronic star map simulator

Cited by 9 publications

References 4 publications

Star centroid extraction in the image sequences of a discrete detector array on geostationary orbit

Star centroid extraction in the image sequences of a discrete detector array on geostationary orbit

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

Field Programmable Gate Array-Based Acceleration Algorithm Design for Dynamic Star Map Parallel Computing

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