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Reverse Engineering Maneuvers

Reverse Engineering Maneuvers. R Hujsak Oct 13, 2005. Unknown maneuver event. Pre-maneuver tracking. Post-maneuver tracking. Predict thru unknown maneuver. Normal OD. Reject data. The problem. Predict thru unknown maneuver. Normal OD. Reject data.

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Reverse Engineering Maneuvers

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  1. Reverse Engineering Maneuvers R Hujsak Oct 13, 2005

  2. Unknown maneuver event Pre-maneuver tracking Post-maneuver tracking Predict thru unknown maneuver Normal OD Reject data The problem

  3. Predict thru unknown maneuver Normal OD Reject data Restart OD process with post-maneuver data Normal OD Stop OD process during maneuver Predict forward Predict backward The usual approach Unknown maneuver event Pre-maneuver tracking Post-maneuver tracking Reconstruction depends on post-maneuver accuracy

  4. Limitations with the usual approach • Accuracy is a function of tracking data • Density & distribution • Timeliness is a function of • Tracking system response to maneuver detection • Assumes impulsive maneuvers • Does not work for longer duration burns • ANIK-F2 thrusting 8 hrs/days • MEXSAT thrusts for 5 days ON, 1 day OFF, 6 days ON • PANAMSAT D4S thrusts for 15 hrs/day • GEO transfer thrust  1 hour Is there a way to handle finite maneuvers?

  5. Predict thru unknown maneuver Normal OD Reject data Use the filter covariance inflate the covariance Adding data refines estimate Postulate various maneuver hypotheses Filter accepts new data & covariance collapses Smoothed ephemeris is predicted backward. Intersection defines maneuver. Filters provide other options Unknown impulsive event Pre-maneuver tracking Post-maneuver tracking

  6. This presentation • Examine alternatives to classical approach • Examine various maneuvers • Simple impulsive burns • Complex duration thrusting • Examine various methods • “Shot-gun” approach • IOD and reverse prediction • Brute force & iterated analysis approach

  7. Concrete examples • Classical method, unknown impulse • GEO unknown EW stationkeeping • HEO unknown impulse perigee burn • XIPS finite maneuvers • Boeing 702 (ANIK-F2 insertion) • DSCS perigee raising finite maneuver • Backups (if there’s time) • LEO single large impulse

  8. GOE EW stationkeeping

  9. GEO unknown EW stationkeeping • Assume 3 tracking stations • Track once per day, each • 5 minute track, range, az, el • Unknown intrack maneuver 1 m/sec • 15 minute track after maneuver • Objectives: Use IOD to help identify maneuver time • Use IOD solution to process through maneuver

  10. Predict thru unknown maneuver Normal OD Reject data Restart OD process with post-maneuver data Normal OD Stop OD process during maneuver Predict forward Predict backward The usual approach Unknown maneuver event Pre-maneuver tracking Post-maneuver tracking Reconstruction depends on post-maneuver accuracy

  11. Maneuver But residual trends do not indicate maneuver time Maneuver detection is easy…

  12. Post-maneuver tracks (enlarged) Residual trends do not indicate maneuver time .. 2 hours < 1/3 rev .. so perform IOD and 3-track least squares (standard orbit analysis).

  13. Least-squares fit & back predict Solution = 1 Jun 2004 15:00:00 Truth = 1 Jun 2004 00:00:00 LS fit to 3 tracks, less than 1 rev of sampling

  14. Least-squares fit & back predict Rdot = 0.1 m/sec Idot = 0.99 m/sec Solution = 1 Jun 2004 15:00:00 Truth = 1 Jun 2004 00:00:00

  15. Add another day of tracking data … Solves the problem: Solution = 1 Jun 2004 00:01 LS fit to 3 tracks, less than 2 revs of sampling

  16. … gives the right answer

  17. General remarks • Classical approach works well • For single impulse • No tracking during thrust • The accuracy of maneuver reconstruction • Depends on the tracking data density • Depends on sampling post-maneuver orbit • Rules of thumb • Can be developed through parametric analyses • Using a simulator, IOD, and Least Squares

  18. Questions on GEO EW Reconstruction?

  19. HEO unknown “perigee” burn

  20. The HEO problem • Tracking during apogee • No tracking through perigee • Small maneuvers at perigee spoil the fit to tracking data • Find a way to “fit through” maneuvers • Then reverse engineer maneuver

  21. Filter rejects tracking data Normal OD (filter) Predict thru unknown maneuver Add “shotgun” V’s Difference ephemerides in STK GUESS Process overview – HEO impulse Unknown maneuver event Pre-maneuver tracking Post-maneuver tracking Filter accepts tracking data Smooth backward Predict Backward Filter & Smooth – Solve for correction to GUESS

  22. Dense tracking schedule • Single ground station (Boston) • Dense tracking 1 ob / 10 minutes

  23. 5 hour data gap Nominal performance without maneuver

  24. Nominal range residuals without maneuver Insert maneuver in 5 hr gap

  25. Simulated maneuver • Tracking gap 3 Jun (7:20 – 12:20) • Simulated delta-v intrack = 0.5 m/sec • Maneuver time = 3 Jun 10:20

  26. Maneuver detection is easy

  27. Filter rejects tracking data Normal OD (filter) Predict thru unknown maneuver Add “shotgun” V’s Process overview – HEO impulse Unknown maneuver event Pre-maneuver tracking Post-maneuver tracking

  28. “Shotgun” maneuver process noise over 5 hours • Over data gap (true maneuver at 10:20) • Insert 5 V impulses at: • 3 Jun 2004 07:30:00.000 UTCG • 3 Jun 2004 08:40:00.000 UTCG • 3 Jun 2004 09:50:00.000 UTCG • 3 Jun 2004 11:00:00.000 UTCG • 3 Jun 2004 12:10:00.000 UTCG • Set VR = VI = VC = 0 • Set process noise magnitude • RDOT = 0.5 m/sec • IDOT = 0.5 m/sec • CDOT = 0.5 m/sec • Run filter and smoother

  29. Filter rejects tracking data Normal OD (filter) Predict thru unknown maneuver Add “shotgun” V’s Process overview – HEO impulse Unknown maneuver event Pre-maneuver tracking Post-maneuver tracking Filter accepts tracking data

  30. Filter processes through maneuver First post-maneuver track (at ~ 2.6 d) Maneuver

  31. Covariance inflated by delta-V’s Almost 80 km First post-maneuver track (at ~ 2.6 d)

  32. Filter rejects tracking data Normal OD (filter) Predict thru unknown maneuver Add “shotgun” V’s Process overview – HEO impulse Unknown maneuver event Pre-maneuver tracking Post-maneuver tracking Filter accepts tracking data Smooth backward Show why this does not identify maneuver time

  33. Significantly reduced from 80 km First post-maneuver track (at ~ 2.6 d) Smoother covariance is much better

  34. Smoother estimates Rdot, Idot, Cdot • (true maneuver at 10:20 with 0.0, 0.5, 0.0 m/s) • Solves for Rdot, Idot, Cdot: • 5 times impulses m/s sigmas m/s • 07:30:00.000 -.03, .07, .0008 .27, .33, .29 • 08:40:00.000 .05, .10, -.0009 .44, .41, .41 • 09:50:00.000 -.05, .13, -.002 .46, .43, .40 • 11:00:00.000 -.05, .15, -.003 .45, .37, .34 • 12:10:00.000 .06, -.06, .001 .42, .13, .47 • Can’t tell where maneuver is, but there is no crosstrack component • Rerun with CDOT = 0

  35. Performance with subsets is similar

  36. Systematic search • (True maneuver at 10:20) • Postulate 3 maneuvers with CDOT = 0 • Case 1 • 07:30:00.000 .10, -.16, 0 .19, .21, 0 • 08:40:00.000 -.07, .14, 0 .42, .40, 0 • 09:50:00.000 -.11, .48, 0 .19, .21, 0 • Case 2 • 08:40:00.000 -.02, .05, 0 .18, .22, 0 • 09:50:00.000 -.03, .20, 0 .44, .39, 0 • 11:00:00.000 .10, .27, 0 .17, .22, 0 • Case 3 • 09:50:00.000 -.04, .32, 0 .19, .23, 0 • 11:00:00.000 .005, .19, 0 .43, .35, 0 • 12:10:00.000 .03, -.01, 0 .30, .07, 0

  37. Remarks – HEO “shotgun” • Disadvantage of V “shotgun” • Can’t really find the time of maneuver with shotgun approach • Can’t reverse engineer maneuver without time of maneuver • Advantages of V “shotgun” • Allows continued operations through maneuver • Rapid return to operational accuracy • So how can we leverage the solution to find the maneuver?

  38. Filter rejects tracking data Normal OD (filter) Predict thru unknown maneuver Add “shotgun” V’s Difference ephemerides in STK Process overview – HEO impulse Unknown maneuver event Pre-maneuver tracking Post-maneuver tracking Filter accepts tracking data Smooth backward Predict Backward How much post-maneuver data is required and what is the maneuver reconstruction?

  39. Closely examine filter response Single measurement eliminates a lot of the orbit error. What if we filter one measurement and predict backward – and compare to forward prediction?

  40. Position differences forward vs backward predictions Zero at 10:42 Truth at 10:20

  41. Velocity differences forward vs backward predictions At 10:42, Rdot = 0.22, Idot = 0.57 These values will cause residual rejection in filter. (A litmus test for good maneuver reconstruction.)

  42. Improve on maneuver time? What if we filter one hour of tracking and predict backward – and compare to forward prediction?

  43. With one hour post-maneuver track Zero at 09:49 Truth at 10:20

  44. With one hour post-maneuver track At 09:49, Rdot = -0.17, Idot = 0.43 These values will also cause residual rejection in filter since the time is not well-determined

  45. With four hour post-maneuver track? What if we filter four hours of tracking and predict backward – and compare to forward prediction?

  46. With four hour post-maneuver track Zero at 10:22 Truth at 10:20

  47. With four hour post-maneuver track At 09:49, Rdot = .01, Idot = 0.508 These values work well in the filter

  48. Resolution of maneuver time

  49. Do we need 4 hours of dedicated tracking? NO !

  50. Reduce tracking schedule • Thinned tracking yields maneuver time of 10:22 • Short track at “rise” • Short track “at apogee” • Short track at “set” • Sparse tracking yields maneuver time of 10:11 • Short track at “rise” • Short track at “set” • Rule of Thumb • 3 tracks over a 1/3 rev is better than 2 tracks

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