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University of Colorado Boulder ASEN 5070: Statistical Orbit Determination I Fall 2014 Professor Brandon A. Jones Lecture 39: Measurement Modeling and Combining State Estimates
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University of Colorado Boulder No lecture quiz next week. Exam 3 Posted Today ◦ In-class Students: Due December 12 by 5pm ◦ CAETE Students: Due 11:59pm (Mountain) on 12/14 Final Project Due December 15 by noon 2
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University of Colorado Boulder Your solutions must be uploaded to D2L as a searchable PDF ◦ Same rules as homework apply in regards to format, code appendices, etc. Open-book, open notes You may use a computer, MATLAB, etc. Honor code rules apply ◦ Do not give or ask for help from your peers The TA has been instructed to redirect all questions to the instructor I can answer questions to clarify what is being asked, but cannot provide guidance on solutions 3
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University of Colorado Boulder 4 Project Q&A
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University of Colorado Boulder 5 Kalman Filter Discussion
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University of Colorado Boulder In the project, we are estimating C D. However, the partial of the measurements w.r.t. C D is zero. How are we able to estimate this parameter in the Kalman filter? 6
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University of Colorado Boulder We use a process noise model where What happens in our filter if Q k = σ 2 I and the second term above is much bigger than the first? 7
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University of Colorado Boulder 8 Modeling Measurements Tapley, Schutz, and Born, Chapter 3 Montenbruck and Gill, Satellite Orbits, Chapter 6
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University of Colorado Boulder One-way Range ◦ Example: GNSS ◦ Signal travels to/from reference from/to satellite 9
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University of Colorado Boulder Two-way Range ◦ Examples: SLR, DSN ◦ Satellite is a relay for signal 10
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University of Colorado Boulder Multi-way Range ◦ Examples: DSN, TDRSS ◦ Multiple satellite and/or ground stations used 11
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University of Colorado Boulder We have been using range and range-rate: In the real world, what is wrong with these equations? 12
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University of Colorado Boulder At best, a signal travels at the speed of light We must approximate the signal propagation time δt Approximately 0.06 seconds for GPS signal to reach Earth A LEO spacecraft will have moved approximately 500 meters in that time 13
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University of Colorado Boulder Assume we have estimates of our satellite trajectory and the reference station/satellite We need to solve for δt No analytic solution so we solve for the correction using iteration 14
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University of Colorado Boulder Start with δt=0 Compute the distance with the satellite state at time t and the reference state at t-δt Given that distance, compute the light propagation time Δδt Set δt=δt+Δδt Continue until Δδt is sufficiently small 15
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University of Colorado Boulder We have taken care of light-time correction assuming the speed of light in a vacuum. Any other things we should account for? ◦ Signal does not always propagate through a vacuum Ionosphere Troposphere Charged particle interactions Solar corona etc. ◦ Coordinate and time systems This requires a very careful treatment in the filter 16
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University of Colorado Boulder Not including accurate coordinate system information creates systematic errors. ◦ Violates our assumption of random errors! ◦ Creates a time-varying bias in the measurement 17 Table courtesy of Bradley, et al., 2011
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University of Colorado Boulder Is it possible to measure range-rate instantaneously? ◦ No! (at least not that I am aware of) ◦ We have to observe this indirectly Instead, we look at the change in a signal over time to approximate the change in velocity. 18
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University of Colorado Boulder Satellite sends pulse at fixed interval 19
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University of Colorado Boulder 20
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University of Colorado Boulder The velocity of the spacecraft affects the frequency of any radar signal ◦ Some of the effects illustrated in Jason Leonard’s talk on using real data Requires us to observe the change in frequency over some period of time ◦ Known as integrated Doppler shift 21 Image Courtesy of WikiCommons
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University of Colorado Boulder The velocity of the spacecraft affects the frequency of any radar signal ◦ Some of the effects illustrated in Jason Leonard’s talk on using real data Requires us to observe the change in frequency over some period of time ◦ Known as integrated Doppler shift 22 Image Courtesy of WikiCommons
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University of Colorado Boulder Assumes: ◦ Linear change in range over integration time ◦ Constant transmission frequency over integration time 23
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University of Colorado Boulder Do I need to perform any light time correction? Is there anything different about this case when compared to range? 24
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University of Colorado Boulder 25 Combining State Estimates
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University of Colorado Boulder A ground station in Maui observed our satellite several times over the past week ◦ Generated a filtered solution using their observations A ground station in Florida also observed the satellite several times over the past week ◦ Generated a filter solution using their observations What is the best approach to fusing this information? 26
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University of Colorado Boulder Treat one solution as the a priori and the other as the observation ◦ Does it matter which one is which? For this case, H=I 27
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University of Colorado Boulder Does not require the additional processing of observations 28
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University of Colorado Boulder 29
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