A High-Stability and Resource-Efficient Centroid Extraction Method for Star Maps

Qi Zhang; Shugang Liu

doi:10.47297/taposatWSP2633-456911.20250612

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A High-Stability and Resource-Efficient Centroid Extraction Method for Star Maps

Volume 6, Issue 12

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A High-Stability and Resource-Efficient Centroid Extraction Method for Star Maps
A High-Stability and Resource-Efficient Centroid Extraction Method for Star Maps
School of Physics and Electronic Science, Hunan University of Science and Technology, Xiangtan, Hunan, 411201, China
Vol. 6, Issue 12, Pages: 48-55(2025)
Published：30 December 2025
DOI：10.47297/taposatWSP2633-456911.20250612
Accepted：

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Zhang Qi,Liu Shugang.A High-Stability and Resource-Efficient Centroid Extraction Method for Star Maps[J].Theory and Practice of Science and Technology,2025,06(12):48-55.
DOI：

Zhang Qi,Liu Shugang.A High-Stability and Resource-Efficient Centroid Extraction Method for Star Maps[J].Theory and Practice of Science and Technology,2025,06(12):48-55. DOI： 10.47297/taposatWSP2633-456911.20250612.

摘要

Abstract

To address the challenges of inadequate stability and excessive resource consumption in stellar centroid extraction for spaceborne star trackers

this paper proposes a single-scan centroid extraction method. The core innovation lies in the integration of an improved Run-Length Encoding (RLE) method with a Dynamic Union-Find (DUF) mechanism

which enables the concurrent execution of connected component labeling and equivalence merging during a single raster scan

significantly minimizing memory access frequency and processing latency. Optimized specifically for stellar imaging characteristics

the method maintains sub-pixel-level accuracy while adapting to irregular star morphologies and complex noise distributions. Experimental results demonstrate that for a 2048×2048 star image

the method achieves a centroid output latency of approximately 10 μs and a per-frame processing time of 20.97 ms. The standard deviation of centroid error is ≤0.1 pixels

and the false detection rate is below 1.5% under high-noise conditions. This work provides a highly resource-efficient FPGA solution for low-latency attitude determination in aerospace applications.

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references

Ding, J., Dai, D., Tan, W., Wang, X., & Qin, S. (2022). Implementation of a real-time star centroid extraction algorithm with high speed and superior denoising ability. Applied Optics, 61(11), 3115-3122.

Wei, X., Tan, W., Li, J., & Zhang, G. (2014). Exposure time optimization for highly dynamic star trackers. Sensors, 14(3), 4914-4931.

Yan, J., Jiang, J., & Zhang, G. (2016). Dynamic imaging model and parameter optimization for a star tracker. Optics Express, 24(6), 5961-5983.

Zhang, G. (2011). Star identification. National Defense Industry Press: Beijing, China, 109.

Liebe, C. C. (2002). Star trackers for attitude determination. IEEE Aerospace and Electronic Systems Magazine, 10(6), 10-16.

Agarwal, S., Hervas-Martin, E., Byrne, J., Dunne, A., Luis Espinosa-Aranda, J., & Rijlaarsdam, D. (2020). An evaluation of low-cost vision processors for efficient star identification. Sensors, 20(21), 6250.

Rijlaarsdam, D., Yous, H., Byrne, J., Oddenino, D., Furano, G., & Moloney, D. (2020). A survey of lost-in-space star identification algorithms since 2009. Sensors, 20(9), 2579.

Wang, X., Sun, C., & Sun, T. (2019). A novel two-step validation algorithm for lost-in-space star identification. IEEE Transactions on Aerospace and Electronic Systems, 56(3), 2272-2279.

Liu, H., Liu, C., Xie, P., Liu, S., Wang, X., Zhang, Y., ... & Zhao, Y. (2024). Stray light analysis and suppression of high-resolution camera line-of-sight variation real-time monitoring system (LoS Var RTMS). Optics Express, 32(14), 24184-24199.

Wang, H., & Xin, Y. (2020). Wavelet-based contourlet transform and kurtosis map for infrared small target detection in complex background. Sensors, 20(3), 755.

Otsu, N. (1975). A threshold selection method from gray-level histograms. Automatica, 11(285-296), 23-27.

Zhang, P., Zhao, Q., Liu, J., & Liu, N. (2014). A brightness-referenced star identification algorithm for aps star trackers. Sensors, 14(10), 18498-18514.

Liu, H. B., Yang, J. K., Wang, J. Q., Tan, J. C., & Li, X. J. (2011). Star spot location estimation using Kalman filter for star tracker. Applied optics, 50(12), 1735-1744.

Wei, M. S., Xing, F., & You, Z. (2018). A real-time detection and positioning method for small and weak targets using a 1D morphology-based approach in 2D images. Light: Science & Applications, 7(5), 18006-18006.

Bai, X., Zhang, S., Du, B., Liu, Z., Jin, T., Xue, B., & Zhou, F. (2011). Survey on dim small target detection in clutter background: wavelet, inter-frame and filter based algorithms. Procedia Engineering, 15, 479-483.

Marin, M., & Bang, H. (2020). Design and simulation of a high-speed star tracker for direct optical feedback control in ADCS. Sensors, 20(8), 2388.

Chianelli, G., Kunalakantha, P., Myhre, M., & Lee, R. S. (2023). A dual-purpose camera for attitude determination and resident space object detection on a stratospheric balloon. Sensors, 24(1), 71.

Jiang, H., & Fan, X. Y. (2012). Centroid locating for star image object by FPGA. Advanced Materials Research, 403, 1379-1383.

Fan, Q. Y., & Zhang, G. J. (2011). Spot centroiding algorithm for discrete noise image and its hardware implementation. Guangxue Jingmi Gongcheng(Optics and Precision Engineering), 19(12), 2992-2998.

Zhao, F., Zhang, L., Zhang, Z. Y., & Lu, H. Z. (2011). A hardware acceleration based algorithm for real-time binary image connected-component labeling. Journal of Electronics & Information Technology, 33(5), 1069-1075.

Flatt, H., Blume, S., Hesselbarth, S., Schunemann, T., & Pirsch, P. (2008, July). A parallel hardware architecture for connected component labeling based on fast label merging. In 2008 International Conference on Application-Specific Systems, Architectures and Processors (pp. 144-149). IEEE.

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