Issue 4 (190), article 3


Kibern. vyčisl. teh., 2017, Issue 4 (190), pp.

Melnichuk S.V., Dr (Engineering),
Researcher of Dynamic Systems Control Ddepartment
Gubarev V.F., Professor,
Dr (Engineering), Professor,
Corresponding Member of NAS of Ukraine,
Head of Dynamic Systems Control Department
Salnikov N.N., (Engineering),
Senior Researcher of Dynamic Systems Control Department
Space Research Institute National Academy of Sciences of Ukraine
and State Space Agency of Ukraine
Acad. Glushkov av. 40, 4/1, 03680, Kyiv 187, Ukraine


Introduction. Autonomous rendezvous and docking is an important technological capability that enables various spacecraft missions. It requires the real-time relative pose estimation i.e. determination of the position and attitude of a target object relative to a chaser. The usage of techniques based on optical measurement has certain advantages at close range phases of docking.
The purpose of the paper is to create a computer vision system, that estimates position and attitude of the target relative to the chaser. To develop the design of a computer vision system and suited mathematical methods. To use a new learning-based method, which can be implemented for the real-time execution with limited computing power.
Methods. A non-standard approach to solving the problem was used. A combination of image processing techniques, machine learning, decision trees and piecewise linear
approximation of functions were used. The tool of informative features computed by images was essentially used.
Results. A two-stage algorithm, which involves training the computer vision system to recognize the attitude and position of the target in a changing lighting environment was developed. The calculation of the camera parameters was carried out to ensure a given accuracy of the solution of the problem.
Conclusion. It was shown that the informative features can be used to create a high-performance on-board system for estimating relative attitude and position. Implementation of the proposed algorithm allows to create a competitive device for docking in space.
Keywords: autonomous rendezvous, uncooperative pose estimation, model-based pose estimation, vision-based pose estimation, computer vision, decision tree, linear approximation, informative features, image processing, machine learning, identification, relative position and attitude estimation.

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  1. Gubarev V.F., et. al. Using Vision Systems for Determining the Parameters of Relative Motion of Spacecrafts. Journal of Automation and Information Sciences, 2016. №11.
    P. 23–39.
  2. Shi J.-F., et. al. Uncooperative Spacecraft Pose Estimation Using an Infrared Camera During Proximity Operations. AIAA Space 2015 Conference and Exposition.
    Issue AIAA 2015–4429. 17 pp.
  3. Kelsey J.M., et. al. Vision-Based Relative Pose Estimation for Autonomous Rendezvous and Docking. 2006 IEEE Aerospace Conference. 20 pp.
  4. Zorich V.A. Mathematical Analysis. Part 2. Moskow: Nauka, 1984. 640 p.
  5. David, P. et. al. SoftPOSIT: Simultaneous Pose and Correspondence Determination. Interational Journal of Computer Vision. 2004. Vol. 59. I. 3. P. 259–284.
  6. Philip N.K., Ananthasayanam M.R. Relative position and attitude estimation and control schemes for the final phase of an autonomous docking mission of spacecraft. Acta Astronautica. 2003. Vol. 52. I. 7. P. 511–522.
  7. Shijie Monocular Vision-based Two-stage Iterative Algorithm for Relative Position and Attitude Estimation of Docking Spacecraft. Chinese Journal of Aeronautics, 2010. Vol. 23. I. 2. P. 204–210.
  8. Vassilieva N.S. Content-based image retrieval methods. Programming and Computer Software. 2009. Vol. 35. № 3. P. 158–180.
  9. Prewitt J.M.S. Object enhancement and extraction. Picture Processing and Psychopictorics, B. Lipkin and A. Rosenfeld. New York: Academic Press. 1970. P. 75–149.
  10. Sobel I., Feldman G. A 3×3 isotropic gradient operator for image processing, presented at a talk at the Stanford Artificial. Project in Pattern Classification and Scene Analysis, R. Duda and P. Hart. Eds.: John Wiley & Sons, 1968. P. 271–272.
  11. Canny, J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1986. № 8(6). P. 679–698.
  12. Arbelaez P., et al. Contour Detection and Hierarchical Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010. P. 898–916.
  13. Bentley J.L. Multidimensional binary search trees used for associative searching. Communications of the ACM. 1975. Vol. 18. I. 9. P. 509–517.
  14. Samet H. The Design and Analysis of Spatial Data Structures. 1990. 493 p.

Received 14.06.2017

Issue 185, article 4


KVT, 2016, Issue 185, pp.35-47

UDC 629.7.05


Simakov V.A., Gubarev V.F., Salnikov N.N., Melnichuk S.V.

Space Research Institute of the National Academy of Science of Ukraine and State Space Agency of Ukraine, Kyiv, Ukraine , , ,

Introduction. Automatic orbital berthing systems require permanent availability of relative position and attitude of a target spacecraft. In the most general case the only source of information is video filming. Extracting mutual disposition parameters from a video frame is based upon special techniques which can be divided into two large groups: feature-based and model-based. Major difference between them is defined by data structure used for the target description (individual points for feature-based approach vs. rigorous visual model for model-based one). This article is devoted to the research of mathematical problem that appears in considering pose estimation for two orbital spacecraft in the presence of wireframe model of the target when only video filming is available.

The purpose of the article is to construct a model-based method that provides fast and accurate estimation of relative position and attitude of the target spacecraft. We discuss possible drawbacks of direct procedures based on straightforward (pixel-wise) image fitting and propose a subtle algorithm which satisfies formulated conditions.

Results. The algorithm composed of three independent parts (initialization, pose refinement and pose tracking) has been developed and tested on simple initial datum. Initialization stage, responding for rough estimation in the absence of preliminary information, has given relatively poor but quite enough accuracy for the aims of initial approximation. Pose refinement stage which is implemented as iterative procedure based on closeness of neighboring frames demonstrated almost total matching with actual values. Pose tracking (state estimation based on equations of motion) was redundant for our simple example as it could not improve the result provided by pose refinement.

Conclusions. Constructed algorithm has been tested on simplified situation and demonstrated very high precision. More realistic conditions including noises and occlusions can bring to corrupted result that should be recovered. This requires introducing additional steps into the algorithm which are reflected in the text. The notable feature of the algorithm is its high modularity which allows each stage to be implemented and configured independently according to available resources and mission requirements.

Keywords: orbital rendezvous, pose estimation, orbital video filming, computer vision.

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  1. Lowe D.G. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 2004, 60 (2), pp. 91–110.
  2. David P. SoftPOSIT: Simultaneous Pose and Correspondence Determination. International Journal of Computer Vision, 2004, 59 (3), pp. 259–284.
  3. Black M.J., Jepson A.D. EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation. International Journal of Computer Vision, 1998, 26 (1), pp. 63–84.
  4. Trefethen L.N., Bau D. Numerical Linear Algebra. Philadelphia: SIAM, 1997, 361p.
  5. Drummond T., Cipolla R. Real-Time Visual Tracking of Complex Structures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24 (7), pp. 932–946.
  6. Hartley R., Zisserman, A. Multiple View Geometry in Computer Vision. 2nd Edition. Cambridge University Press, 2004, 655p.
  7. Paterson M.S., Yao, F.F. Efficient Binary Space Partitions for Hidden-Surface Removal and Solid Modeling. Discrete and Computational Geometry, 1990, 5, pp. 485–503.
  8. Kelsey J.M., et. al. Vision-Based Relative Pose Estimation for Autonomous Rendezvous and Docking. 2006 IEEE Aerospace Conference. — 20 p.
  9. Wenfu X., et. al. Autonomous Rendezvous and Robotic Capturing of Non-Cooperative Target in Space. Robotica, 2010, 28, pp. 705–718.
  10. Fehse W. Automated Rendezvous and Docking of Spacecraft. Cambridge University Press, 2003, 495 p.

Received 10.06.16

ISSUE 181, article 5


Kibern. vyčisl. teh., 2015, Issue 181, pp.

Melnichuk S.V.

Space Research Institute National Academy of Sciences of Ukraine and State Space Agency of Ukraine


Introduction. One of the important directions in the identification of linear systems are frequency domain methods. In recent decades a finite-frequency approach, focused on the use under bounded uncertainty has been developed. Within finite-frequency approach a method, that allows to construct models with reduced dimensionality has been proposed. The method includes a step of structural identification with regularization by model dimension. This method was used to identify single-input single-output (SISO) systems, so it could not be applied to systems with multiple input and multiple output (MIMO).
Purpose. In order to generalize the method it is proposed to identify SISO models of subsystems, that describes individual inputs and outputs, and then combine them. The main purpose of research is to develop an algorithm, that combine separate SISO models into one general MIMO model.
Results. Separate SISO models determined by their invariant properties. As simple combination of SISO models leads to a MIMO model of large dimension, and some invariant properties in different models may be similar, it makes sense to carry out unification by equating this invariants.
Possibility of association for different combinations of SISO models, that have the same eigenvalues were investigated. It is shown that by combining models additional dependencies between coefficients may be imposed. It is shown that if the dependency graph contains no cycles, then the union is possible. On the basis of this fact the synthesizing algorithm was proposed.
Conclusions. The proposed identification algorithm builds the general MIMO model from separate SISO models so that the dimension of resulting model may be significantly less, than sum of dimensions of original SISO models. The proposed algorithm saves all invariant characteristics of the original models, so approximation accuracy by the each input-output relation is stored.

Keywords: System identification, frequency domain, structural identification, reduced dimensionality.

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  1. Orlov Y.F. Frequency Parameter-Based Identification at Parallel Testing. Automation
    and Remote Control, 2007, vol.68, no. 1. pp. 18–37.
  2. Orlov Y.F. Frequency Parameter-Based Identification at Parallel Testing. Avtomatika
    i Telemekhanika, 2007, vol.68, no. 1. pp. 20–40 (in Russian).
  3. Alexandrov A.G. Method of Frequency Parameters. Avtomatika i Telemekhanika, 1989,
    vol 50, no. 12. pp. 3–15 (in Russian).
  4. Alexandrov A.G., Orlov Y.F. Finite-frequency identification of multidimensional objects.
    2-nd Russian-Swedish Control Conference. St. Petersburg, 1995, pp. 65–69 (in Russian).
  5. Gubarev V.F., Melnychuk S.V. Identification of Multivariable Systems Using Steady-State Parameters. Journal of Automation and Information Sciences, 2012, vol. 44. i. 9. pp. 24–42.
  6. Gubarev V.F., Melnychuk S.V. Identification of Multivariable Systems Using Steady-State Parameters. Journal of Automation and Information Sciences, 2012, no 5. pp. 26–42
    (in Russian).
  7. Melnychuk S.V. Regularity Investigation For Multidimensional System Identification Problem by the Frequency Method. Cybernetics and Computer Engineering, 2014, no 176. pp. 19–33 (in Russian).
  8. Melnychuk S.V. Modified Frequency Method of Structural-Parametric System Identification. Journal of Automation and Information Sciences, 2015, no 4. pp. 27–36 (in Russian).

Received 05.06.2015