Volume 48 Issue 1
Jan.  2019
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Article Navigation >  Infrared and Laser Engineering  > 2019  >  48(1): 126004-0126004(8)

Zhang Zixuan, Jia Jianjun, Qiang Jia, Zhang Liang, Li Jianhua. Research on space-based high precision algorithm for non-cooperative satellite[J]. Infrared and Laser Engineering, 2019, 48(1): 126004-0126004(8). doi: 10.3788/IRLA201948.0126004
Citation: Zhang Zixuan, Jia Jianjun, Qiang Jia, Zhang Liang, Li Jianhua. Research on space-based high precision algorithm for non-cooperative satellite[J]. Infrared and Laser Engineering, 2019, 48(1): 126004-0126004(8). doi: 10.3788/IRLA201948.0126004
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Research on space-based high precision algorithm for non-cooperative satellite

doi: 10.3788/IRLA201948.0126004
  • 1.

    Key Laboratory of Space Active Opto-electronic Technology,Shanghai Institute of Technical Physics,Chinese Academy of Sciences,Shanghai 200083,China;

  • 2.

    University of Chinese Academy of Sciences,Beijing 100049,China;

  • 3.

    Institute of Space Long March Vehicle,Beijing 100076,China

  • Received Date: 2018-08-05
  • Rev Recd Date: 2018-09-03
  • Publish Date: 2019-01-25
  • The space-based non-cooperative target tracking technology could play a crucial part in many aspects of the space application. Currently most well-behaved algorithms were based on video stream. Owing to different working conditions, their tracking precision, speed of operation, warning rate and false alarm rate were dissatisfied with the requirement of space-based satellite tracking systems and missions. Furthermore, the video stream tracking algorithms were too complicated for space-based conditions, where the processors were weaker than those on the ground. To solve these problem, an algorithm based on image correlation, curve fitting, Kalman filter, and SURF algorithm and combined with prediction,tracking and rectification systems was proposed. The algorithm could achieve high speed and high accuracy and be satisfied with the space-based computing environment. Proved by the image simulation experiment and semi-physics simulation, this algorithm could continuously track the target rotated in in-plane arbitrary angle, scaled from 0.4 to 2.1, and handle illumination change. The mean error of image tracking simulation experiment result was lower than 0.9 pixel and frame rate was more than 200 frames per second under most conditions. This algorithm could also deal with image blur, Gaussian noise and salt and pepper noise. Satellite model tracking experiment results showed that the algorithm also had a stably tracking performance for practical satellite model.
  • [1] Liu L R. Laser communications in space I optical link and terminal technology[J]. Chinese Journal of Lasers, 2007, 34(1):3-20. (in Chinese)
    [2] Henruques J F, Rui C, Martins P, et al. Exploiting the circulant structure of tracking-by-detection with lernels[C]//Computer Vision-ECCV 2012, 2012:702-715.
    [3] Henruques J F, Caseiro R J, Martins, et al. High-speed tracking with kemelized correlation filters[J]. IEEE Transactions on Pattern Analysis Machine Intelligence,2015, 37(3):583-596.
    [4] Hong Z, Chen Z, Wang C, et al. Multi-store tracker (MUSTer):A cognitive psychology inspired approach to object tracking[C]//IEEE, Computer Vision and Pattern Recognition, 2015:749-758.
    [5] Danelljan M, Hager G, Khan F S, et al. Learning spatially regularized correlation filters for visual tracking[C]//IEEE International Conference on Computer Vision. IEEE, 2015:4310-4318.
    [6] Danelljan M, Bhat G, Khan F S, et al. ECO:efficient convolution operators for tracking[C]//IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2017:6931-6939.
    [7] Nam H, Han B. Learning multi-domain convolutional neural networks for visual tracking[C]//Computer Vision and Pattern Recognition, IEEE, 2016:4293-4302.
    [8] Huang H B, Ai Y, Chen J, et al. Image recognition and tracking systen for high frame frequency object based on Kalman predictor and fuzzy PID technology[J]. Acta Photonica Sinica, 2009, 38(12):3295-3300.
    [9] Bay H, Tuytelaars T, Gool L V. SURF:speeded up robust features[J]. Computer Vision and Image Understanding, 2008, 110(3):346-359.
    [10] Shen T S, Zhang J, Lou S L. Precise registration of space observation images for target detection[J]. Optics and Precision Engineering, 2014, 22(8):2205-2213. (in Chinese)
    [11] Zhou Yuhaowei. Embedded multichannel real-Time images processing system based on DSP platform[D]. Shanghai:Shanghai Jiao Tong University, 2013:61-63. (in Chinese)
    [12] Yang M D, Jia J J, Qiang J, et al. Study of image matching algorithm and sub-pixel fitting algorithm in target tracking[C]//Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics 2014. 2015, 9521:95211M.
    [13] Yang Mingdong, Wang Jianyu, Jia Jianjun, et al. Research on technologies of space area targets high-precision tracking based on SWAD algorithm[J]. Infrared and Laser Engineering, 2016, 45(2):0228002(in chinese)
    [14] Xing Yunlong, Li Aihua, Cui Zhigao, et al. Moving target tracking algorithm based on improved Kernelized correlation filter[J]. Infrared and Laser Engineering, 2016, 45(s1):S126004. (in Chinese)
    [15] Zhang Lei, Wang Yanjie, Sun Honghai, et al. Adaptive scale object tracking with kernelized correlation filters[J]. Optics and Precision Engineering, 2016, 24(2):448-459. (in Chinese)
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Research on space-based high precision algorithm for non-cooperative satellite

doi: 10.3788/IRLA201948.0126004
  • 1. Key Laboratory of Space Active Opto-electronic Technology,Shanghai Institute of Technical Physics,Chinese Academy of Sciences,Shanghai 200083,China;
  • 2. University of Chinese Academy of Sciences,Beijing 100049,China;
  • 3. Institute of Space Long March Vehicle,Beijing 100076,China
  • Received Date: 2018-08-05
  • Rev Recd Date: 2018-09-03
  • Publish Date: 2019-01-25

Abstract: The space-based non-cooperative target tracking technology could play a crucial part in many aspects of the space application. Currently most well-behaved algorithms were based on video stream. Owing to different working conditions, their tracking precision, speed of operation, warning rate and false alarm rate were dissatisfied with the requirement of space-based satellite tracking systems and missions. Furthermore, the video stream tracking algorithms were too complicated for space-based conditions, where the processors were weaker than those on the ground. To solve these problem, an algorithm based on image correlation, curve fitting, Kalman filter, and SURF algorithm and combined with prediction,tracking and rectification systems was proposed. The algorithm could achieve high speed and high accuracy and be satisfied with the space-based computing environment. Proved by the image simulation experiment and semi-physics simulation, this algorithm could continuously track the target rotated in in-plane arbitrary angle, scaled from 0.4 to 2.1, and handle illumination change. The mean error of image tracking simulation experiment result was lower than 0.9 pixel and frame rate was more than 200 frames per second under most conditions. This algorithm could also deal with image blur, Gaussian noise and salt and pepper noise. Satellite model tracking experiment results showed that the algorithm also had a stably tracking performance for practical satellite model.

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