CLC number: V447
On-line Access: 2020-11-13
Received: 2019-10-30
Revision Accepted: 2020-04-14
Crosschecked: 2020-07-20
Cited: 0
Clicked: 4080
Hao Wang, Zhi-yuan Wang, Ben-dong Wang, Zhuo-qun Yu, Zhong-he Jin, John L. Crassidis. An artificial intelligence enhanced star identification algorithm[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.1900590 @article{title="An artificial intelligence enhanced star identification algorithm", %0 Journal Article TY - JOUR
一种基于人工智能的星图识别算法王昊1,王志远1,王本冬1,于卓群1,金仲和1,John L.CRASSIDIS2 1浙江大学航空航天学院,中国杭州市,310027 2纽约州立大学布法罗分校机械与航天工程系,美国纽约州艾摩斯特市,14260-4400 摘要:针对星敏感器在姿态失锁状态下的星图识别问题,提出一种基于人工智能的星图识别算法。该方法基于Vgg16的卷积神经网络模型对星图分类。为达到最优性能,构建了一个星图训练集。仿真结果表明该算法对星图识别问题中的多种噪声具有强鲁棒性,包括星点位置噪声、星等噪声、伪星以及星敏感器角速度。在多种噪声影响下,该方法的识别率依然保持在96%,相比传统的金字塔形算法和栅格算法有显著提升。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[1]Aghaei M, Moghaddam HA, 2016. Grid star identification improvement using optimization approaches. IEEE Trans Aerosp Electron Syst, 52(5):2080-2090. [2]Cole CL, Crassidis JL, 2006. Fast star-pattern recognition using planar triangles. J Guid Contr Dynam, 29(1):64-71. [3]Cheng J, Wang PS, Li G, 2018. Recent advances in efficient computation of deep convolutional neural networks. Front Inform Technol Electron Eng, 19(1):64-77. [4]Hancock BR, Stirbl RC, Cunningham TJ, et al., 2020. CMOS active pixel sensor specific performance effects on star tracker/imager position accuracy. Symp on Integrated Optics, p.43-53. [5]Hernández EA, Alonso MA, Chávez E, et al. 2017. Robust polygon recognition method with similarity invariants applied to star identification. Adv Space Res, 59(4):1095-1111. [6]Hong J, Dickerson JA, 2000. Neural-network-based autonomous star identification algorithm. J Guid Contr Dynam, 23(4):728-735. [7]Jia YQ, Shelhamer E, Donahue J, et al., 2014. Caffe: convolutional architecture for fast feature embedding. Proc 22nd ACM Int Conf on Multimedia, p.675-678. [8]Jing Y, Liang W, 2012. An improved star identification method based on neural network. Proc IEEE 10th Int Conf on Industrial Informatics, p.118-123. [9]Juang JN, Kim HY, Junkins JL, 2003. An efficient and robust singular value method for star pattern recognition and attitude determination. NASA/TM-2003-212142, NASA Langley Research Center, Hampton, USA. [10]Kim K, Bang H, 2020. Algorithm with patterned singular value approach for highly reliable autonomous star identification. Sensors, 20(2):374. [11]Krizhevsky A, Sutskever I, Hinton GE, 2017. ImageNet classification with deep convolutional neural networks. Commun ACM, 60(6):94-90. [12]Kruzhilov IS, 2012. Evaluation of instrument stellar magnitudes without recourse to data as to star spectral classes. J Appl Remote Sens, 6(1):063537. [13]Kumar M, Mortari D, Junkins JL, 2010. An analytical approach to star identification reliability. Acta Astronaut, 66(3-4):508-515. [14]LeCun Y, Bengio Y, Hinton G, 2015. Deep learning. Nature, 521(7553):436-444. [15]Liebe CC, 1992. Pattern recognition of star constellations for spacecraft applications. IEEE Aerosp Electron Syst Mag, 7(6):34-41. [16]Liu B, Dai Y, Li X, et al., 2003. Building text classifiers using positive and unlabeled examples. Proc 3rd IEEE Int Conf on Data Mining, p.179-188. [17]Mehta DS, Chen SS, Low KS, 2018. A robust star identification algorithm with star shortlisting. Adv Space Res, 61(10):2647-2660. [18]Mortari D, Samaan MA, Bruccoleri C, 2004. The pyramid star identification technique. Navigation, 51(3):171-183. [19]Na M, Zheng DN, Jia PF, 2009. Modified grid algorithm for noisy all-sky autonomous star identification. IEEE Trans Aerosp Electron Syst, 45(2):516-522. [20]Opromolla R, Fasano G, Rufino G, et al., 2017. A new star tracker concept for satellite attitude determination based on a multi-purpose panoramic camera. Acta Astronaut, 140:166-175. [21]Padgett C, Kreutz-Delgado K, 1997. A grid algorithm for autonomous star identification. IEEE Trans Aerosp Electron Syst, 33(1):202-213. [22]Qiu JT, Wang J, Yao S, et al., 2016. Going deeper with embedded FPGA platform for convolutional neural network. Proc ACM/SIGDA Int Symp on Field-Programmable Gate Arrays, p.26-35. [23]Quan W, Fang JC, 2010. A star recognition method based on the adaptive ant colony algorithm for star sensors. Sensors, 10(3):1955-1966. [24]Roberts P, Walker R, 2005. Application of a counter propagation neural network for star identification. AIAA Guidance, Navigation, and Control Conf and Exhibit, p.15-18. [25]Roshanian J, Yazdani S, Ebrahimi M, 2016. Star identification based on Euclidean distance transform, Voronoi tessellation, and k-nearest neighbor classification. IEEE Trans Aerosp Electron Syst, 52(6):2940-2949. [26]Samirbhai MD, Chen S, Low KS, 2019. A hamming distance and spearman correlation based star identification algorithm. IEEE Trans Aerosp Electron Syst, 55(1):17-30. [27]Schiattarella V, Spiller D, Curti F, 2017. A novel star identification technique robust to high presence of false objects: the multi-poles algorithm. Adv Space Res, 59(8):2133-2147. [28]Simonyan K, Zisserman A, 2015. Very deep convolutional networks for large-scale image recognition. https://arxiv.org/abs/1409.1556 [29]Spratling BB IV, Mortari D, 2009. A survey on star identification algorithms. Algorithms, 2(1):93-107. [30]Sun ZP, Qi XD, Jin G, et al., 2017. Ellipticity pivot star method for autonomous star identification. Optik, 137:1-5. [31]Szegedy C, Liu W, Jia YQ, et al., 2015. Going deeper with convolutions. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.1-9. [32]Wang XC, Sun CH, Sun T, 2019. A novel 2-step validation algorithm for lost-in-space star identification. IEEE Trans Aerosp Electron Syst, 56(3):2272-2279. [33]Wei X, Wen DS, Song ZX, et al., 2019. A star identification algorithm based on radial and dynamic cyclic features of star pattern. Adv Space Res, 63(7):2245-2259. [34]Zhang GJ, 2017. Star identification: methods, techniques, and algorithms. Springer, Berlin, Germany. [35]Zhang GJ, Wei XG, Jiang J, 2008. Full-sky autonomous star identification based on radial and cyclic features of star pattern. Image Vis Comput, 26(7):891-897. [36]Zhang QS, Zhu SC, 2018. Visual interpretability for deep learning: a survey. Front Inform Technol Electron Eng, 19(1):27-39. Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
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