[논문]Precise Orbit Determination Based on the Unscented Transform for Optical Observations

Hwang, Hyewon; Lee, Eunji; Park, Sang-Young

doi:10.5140/jass.2019.36.4.249

[국내논문] Precise Orbit Determination Based on the Unscented Transform for Optical Observations 원문보기

Journal of astronomy and space sciences, v.36 no.4, 2019년, pp.249 - 264

Hwang, Hyewon (Department of Astronomy, Yonsei University) , Lee, Eunji (Department of Astronomy, Yonsei University) , Park, Sang-Young (Department of Astronomy, Yonsei University)

Abstract ▼ AI-Helper

In this study, the precise orbit determination (POD) software is developed for optical observation. To improve the performance of the estimation algorithm, a nonlinear batch filter, based on the unscented transform (UT) that overcomes the disadvantages of the least-squares (LS) batch filter, is utilized. The LS and UT batch filter algorithms are verified through numerical simulation analysis using artificial optical measurements. We use the real optical observation data of a low Earth orbit (LEO) satellite, Cryosat-2, observed from optical wide-field patrol network (OWL-Net), to verify the performance of the POD software developed. The effects of light travel time, annual aberration, and diurnal aberration are considered as error models to correct OWL-Net data. As a result of POD, measurement residual and estimated state vector of the LS batch filter converge to the local minimum when the initial orbit error is large or the initial covariance matrix is smaller than the initial error level. However, UT batch filter converges to the global minimum, irrespective of the initial orbit error and the initial covariance matrix.

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표/그림 (28)

그림 Fig. 1. Flowchart of the precise orbit determination (POD) software.
그림 Fig. 2. Motion of the satellite within the geocentric and topocentric equatorial coordinate system.
그림 Fig. 3. 24-hour propagation error in MATLAB and GMAT considering only the Earth gravitational model (left) and all perturbations including the atmospheric drag model, the solar radiation pressure, and the lunar/solar gravity (right). GMAT, general mission analysis tool.
표 Table 1. CPF residuals (1σ) before and after observational data correction
그림 Fig. 4. CPF residuals before (gray) and after (black) correction of arc 1 data. CPF, consolidated prediction format.
그림 Fig. 5. Artificial optical observation data generated by GMAT. GMAT, general mission analysis tool.
표 Table 2. Initial orbit error and initial covariance matrix of six cases for POD analysis
표 Fig. 6. Position errors of the LS and UT batch filters according to the initial orbit error using artificial data, which converged to approximately 10 m. LS, least-squares; UT, unscented transform.
표 Table 3. Estimated position error in the topocentric RTN coordinate system of LS batch filter with artificial data according to the initial orbit error
그림 Fig. 7. Observation data (RA/DEC) of Cryosat-2 observed in Korea (black) and USA (gray). RA, right ascension; DEC, declination.
표 Table 4. Estimated position error in the topocentric RTN coordinate system of UT batch filter with artificial data according to the initial orbit error
표 Table 5. OWL-Net observation data information for Cryosat-2 acquired in April-May 2018
그림 Fig. 8. Position errors of the IOD results using OWL-Net data evaluated by comparing with CPF ephemeris (red lines: 1, 2, 3σ). IOD, initial orbit determination; OWL-Net, optical wide-field patrol network; CPF, consolidated prediction format.
표 Table 6. The parameters used for the estimation of LS and UT batch filters
그림 Fig. 9. Post-fit residuals of the LS (gray) and UT (black) batch filters for case 1 of the initial orbit error analysis. LS, least-squares; UT, unscented transform.
표 Table 7. Position error of initial guess state vector with respect to CPF for analysis on the effect of initial error
표 Table 8. Post-fit residual of POD for analysis on the effect of initial error
그림 Fig. 10. Post-fit residuals of the LS (gray) and UT (black) batch filters for case 4 of the initial orbit error analysis. LS, least-squares; UT, unscented transform.
그림 Fig. 11. Position errors of LS (upper) and UT (bottom) batch filters according to the initial orbit error using OWL-Net arc 1 data evaluated by comparing with CPF ephemeris. LS, least-squares; UT, unscented transform; OWL-Net, optical wide-field patrol network; CPF, consolidated prediction format.
표 Table 9. Estimated position error in the topocentric RTN coordinate system of LS batch filter according to the initial orbit error
표 Table 10. Estimated position error in the topocentric RTN coordinate system of UT batch filter according to the initial orbit error
표 Table 11. Position error of initial guess state vector with respect to TLE for analysis on the effect of initial error
표 Table 12. Velocity error of initial guess state vector with respect to TLE for analysis on the effect of initial error
그림 Fig. 12. Position errors of the LS (upper) and UT (bottom) batch filters according to the covariance matrix using OWL-Net arc 1 data evaluated by comparing with CPF ephemeris. LS, least-squares; UT, unscented transform; OWL-Net, optical wide-field patrol network; CPF, consolidated prediction format.
표 Table 13. Post-fit residuals of POD for analysis on the effect of initial covariance matrix
그림 Fig. 13. Position errors of the LS (upper) and UT (bottom) batch filters according to the arc length and the number of data pints using OWL-Net arc 1–4 data evaluated by comparing with CPF ephemeris. LS, least-squares; UT, unscented transform; OWL-Net, optical wide-field patrol network; CPF, consolidated prediction format.
표 Table 14. Position error of initial guess state vector with respect to CPF for analysis on the effect of arc length and the number of data points
표 Table 15. Post-fit residual of POD for analysis on the effect of arc length and the number of data points

참고문헌 (15)

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상세보기
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상세보기
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