- Page 1 and 2: Improved Space Target Tracking Thro
- Page 3 and 4: DEDICATION I dedicate this disserta
- Page 5 and 6: TABLE OF CONTENTS Page LIST OF TABL
- Page 7 and 8: LIST OF TABLES Table Page Table 1.
- Page 9 and 10: Figure 29. Mean error of target pos
- Page 11 and 12: u Deterministic input g Gravitation
- Page 13 and 14: The system then utilizes a separate
- Page 15 and 16: Using higher altitude to gain advan
- Page 17 and 18: Associate tracks from other element
- Page 19 and 20: For the nominal scenario Table 1 pr
- Page 21 and 22: 1.3. Purpose of the Study The curre
- Page 23 and 24: section. In a missile defense scena
- Page 25 and 26: As mentioned above, errors in satel
- Page 27 and 28: 2. STATE OF THE ART From the early
- Page 29 and 30: 2.1.1. The Kalman Filter The Kalman
- Page 31 and 32: 2.1.4. The Unscented Kalman Filter
- Page 33 and 34: y accuracy of an initial guess of t
- Page 35 and 36: the state vector in some additive m
- Page 37 and 38: . Orbital Coordination Frame (O-Fra
- Page 39 and 40: e. Telescope Pointing Frame (T-fram
- Page 41 and 42: an object sighting message (OSM). T
- Page 43: vector until a new measurement is m
- Page 47 and 48: 3.2.2. Target model For this study
- Page 49 and 50: 4. BIAS MEASUREMENT AND CORRECTION
- Page 51 and 52: Table 4. Mean error in target posit
- Page 53 and 54: otation axis and measures azimuth d
- Page 55 and 56: For this study, the Aura catalog wa
- Page 57 and 58: position of the satellite. This ang
- Page 59 and 60: As target tracking proceeds there w
- Page 61 and 62: Latitude about a 0.25 and 0.5 Hertz
- Page 63 and 64: 4.4. Bias modeling and measurement
- Page 65 and 66: β az = tan −1 Δz ex − Δz act
- Page 67 and 68: and β k = β k−1 + K β z β k
- Page 69 and 70: Bias (rads) Bias measurements witho
- Page 71 and 72: Using a test scenario we studied th
- Page 73 and 74: Mean Error (m) 800 700 Mean Error o
- Page 75 and 76: z X S 2 v 2 y v 1 i S 1 i Figure
- Page 77 and 78: independent estimate from each sens
- Page 79 and 80: z k = (x k ) + v k (5-12) where h(x
- Page 81 and 82: the = 90 degrees, the ellipse beco
- Page 83 and 84: to be white and independent with re
- Page 85 and 86: where (L + λ)P x is the ith row or
- Page 87 and 88: 5.2.6. Sensor Fusion Following the
- Page 89 and 90: Mean Error Km Table 11. Mean error
- Page 91 and 92: Mean Error Km Mean Error Km 0.8 0.7
- Page 93 and 94: Mean Error Km Mean Error Km 1 0.9 0
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Mean Error Km Mean Error Km 1 0.9 0
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6. FILTER PERFORMANCE MEASURES In t
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J z k = E M J z k: M = M p M J z (k
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Error variance (km)2 Error variance
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epsilon epsilon 6000 5000 4000 3000
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Each scenario test, with the except
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Furthermore, the bias correction ha
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sampling, we tested two different b
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spacecraft attitude, or the measure
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first was using the intercept point
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also be tested on maneuvering targe
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z APPENDIX A: SIMULATION RESULTS Sc
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Mean Error Km Mean Error Km 0.8 0.7
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Bias (rads) Bias (rads) Sensor 1 Bi
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Mean Error Km Bias (rads) 3 x 10-4
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Bias (rads) Mean Error Km 1 0.9 Mea
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114 NEA-SF-DEVELOP Case 1 Filter ty
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116 NEA-SF-DEVELOP Time step = 5 se
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Bias (rads) Bias (rads) Bias measur
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Latitude 50 Sensor tracks and star
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z Scenario: Northeast Asia to San F
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Mean Error Km Mean Error Km 2 1.8 1
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126 NEA-SF-D Case 1 Filter type wit
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Bias (rads) Bias (rads) Bias measur
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Latitude Sensor tracks and star det
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z Scenario: Northeast Asia to Hawai
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Mean Error Km Mean Error Km 2 1.8 1
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136 NEA-HI-D Case 1 Filter type wit
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Bias (rads) Bias (rads) Bias measur
- Page 153 and 154:
Latitude Sensor tracks and star det
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z Scenario: Northeast Asia to Guam
- Page 157 and 158:
Mean Error Km Mean Error Km 2 1.8 1
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146 NEA-GU-D Case 1 Filter type wit
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Mean Error Km Bias (rads) Bias meas
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z Scenario: Iridium Satellite track
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Latitude Mean Error Km 2 1.8 1.6 Me
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APPENDIX B: SELECTED MATLAB PROGRAM
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end % t end % bias_senario_case = 2
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Tcount2 = 0; % --------------------
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nearest_1 = NNtable_1(1,Q(11)); end
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Tfirst2 = 0; Tcount2 = 0; end % end
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else errorstatement = 'star_case er
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% observation and Satellite positio
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error(kk, b).int_EKF_MSE = sum(erro
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data = results.error(b+1,:) stars =
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Bias Extended Kalman Filter functio
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Linearized Kalman Filter Function f
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APPENDIX C: ORBITOLOGY BASICS The f
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REFERENCES 178
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[13] Chang, C. B. "Ballistic Trajec
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[40] Sande, C., and N. Ottenstein.
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Hue, Carine, Jean-Pierre le Cadre,