ANCS Wiki - User contributions [en]

Geometric Integration

2012-11-20T17:09:04Z

Msandrle:

==What is Geometric Integration?==

Geometric integration employs a numerical method that preserves geometric or group properties of the exact differential equation solution trajectory.

==Motivation==

[[File:sat_cloud.png|thumbnail|right|150px|Alt=SSA|Space Object Cloud]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|380px|Alt=Error Tradeoff|Truncation and Roundoff Error Tradeoff]]
In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

For small time-steps (< 1s) the CG geometric approach did not perform as well as classical Runge-Kutta (RK) methods. Geometric approaches require more calculations and in this step-size regime, roundoff error and machine precision dominate.

Significant improvement is observed using a fourth-order CG algorithm with larger time-steps (~> 1s). The quaternion norm constraint is maintained essentially to within machine precision and attitude errors are reduced by approximately two orders of magnitude.

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]
The results are particularly important for high fidelity attitude dynamics. Most perturbing forces, such as drag, solar radiation pressure, magnetic forces and gravity gradients, are a direct function of attitude. Attitude dynamics are much faster than orbital dynamics and therefore dictate the integration time-step in any simulator. Improvements made using a geometric integration method significantly increased accuracy at larger time-steps. The great computational cost of high fidelity models and long-term propagation intervals (days, weeks or months) is therefore reduced.

*[[Attribute_Estimation|Attribute Estimation]]
[[File:shape.png|thumbnail|right|300px|Alt=Satellite Shape|Perturbing Forces Depend on Attitude]]
In applications involving satellite attribute estimation, the size, shape, surface materials and attitude may be unknown. Accurate attitude estimate propagation between measurement updates is essential. Falling well within the domain of SSA, satellite attribute estimation suffers from the same challenges: limited sensor time due to over-tasking, infrequent pass opportunities and longer propagation intervals. While sparse data must be used most effectively, attitude propagation between measurement updates must be as accurate as possible.

==References==

Andrle, M.S., and Crassidis, J.L., “[http://ancs.eng.buffalo.edu/pdf/ancs_papers/2012/geom_int.pdf Geometric Integration of Quaternions],” AIAA/AAS Astrodynamics Specialist Conference, Minneapolis, MN, Aug. 2012, AIAA Paper #2012-4421.

Geometric Integration

2012-11-20T17:07:53Z

Msandrle:

Geometric Integration

2012-11-13T20:54:44Z

Msandrle: /* What is Geometric Integration? */

==What is Geometric Integration?==

Geometric integration employs a numerical method that preserves geometric or group properties of the exact differential equation solution trajectory.

==Motivation==

[[File:sat_cloud.png|thumbnail|right|150px|Alt=SSA|Space Object Cloud]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Truncation and Roundoff Error Tradeoff]]
In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

For small time-steps (< 1s) the CG geometric approach did not perform as well as classical Runge-Kutta (RK) methods. Geometric approaches require more calculations and in this step-size regime, roundoff error and machine precision dominate.

Significant improvement is observed using a fourth-order CG algorithm with larger time-steps (~> 1s). The quaternion norm constraint is maintained essentially to within machine precision and attitude errors are reduced by approximately two orders of magnitude.

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]
The results are particularly important for high fidelity attitude dynamics. Most perturbing forces, such as drag, solar radiation pressure, magnetic forces and gravity gradients, are a direct function of attitude. Attitude dynamics are much faster than orbital dynamics and therefore dictate the integration time-step in any simulator. Improvements made using a geometric integration method significantly increased accuracy at larger time-steps. The great computational cost of high fidelity models and long-term propagation intervals (days, weeks or months) is therefore reduced.

*Attribute Estimation
[[File:shape.png|thumbnail|right|300px|Alt=Satellite Shape|Perturbing Forces Depend on Attitude]]
In applications involving satellite attribute estimation, the size, shape, surface materials and attitude may be unknown. Accurate attitude estimate propagation between measurement updates is essential. Falling well within the domain of SSA, satellite attribute estimation suffers from the same challenges: limited sensor time due to over-tasking, infrequent pass opportunities and longer propagation intervals. While sparse data must be used most effectively, attitude propagation between measurement updates must be as accurate as possible.

==References==

Andrle, M.S., and Crassidis, J.L., “[http://ancs.eng.buffalo.edu/pdf/ancs_papers/2012/geom_int.pdf Geometric Integration of Quaternions],” AIAA/AAS Astrodynamics Specialist Conference, Minneapolis, MN, Aug. 2012, AIAA Paper #2012-4421.

Geometric Integration

2012-11-13T20:30:05Z

Msandrle: /* Findings */

==What is Geometric Integration?==

==Motivation==

[[File:sat_cloud.png|thumbnail|right|150px|Alt=SSA|Space Object Cloud]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Truncation and Roundoff Error Tradeoff]]
In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

For small time-steps (< 1s) the CG geometric approach did not perform as well as classical Runge-Kutta (RK) methods. Geometric approaches require more calculations and in this step-size regime, roundoff error and machine precision dominate.

Significant improvement is observed using a fourth-order CG algorithm with larger time-steps (~> 1s). The quaternion norm constraint is maintained essentially to within machine precision and attitude errors are reduced by approximately two orders of magnitude.

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]
The results are particularly important for high fidelity attitude dynamics. Most perturbing forces, such as drag, solar radiation pressure, magnetic forces and gravity gradients, are a direct function of attitude. Attitude dynamics are much faster than orbital dynamics and therefore dictate the integration time-step in any simulator. Improvements made using a geometric integration method significantly increased accuracy at larger time-steps. The great computational cost of high fidelity models and long-term propagation intervals (days, weeks or months) is therefore reduced.

*Attribute Estimation
[[File:shape.png|thumbnail|right|300px|Alt=Satellite Shape|Perturbing Forces Depend on Attitude]]
In applications involving satellite attribute estimation, the size, shape, surface materials and attitude may be unknown. Accurate attitude estimate propagation between measurement updates is essential. Falling well within the domain of SSA, satellite attribute estimation suffers from the same challenges: limited sensor time due to over-tasking, infrequent pass opportunities and longer propagation intervals. While sparse data must be used most effectively, attitude propagation between measurement updates must be as accurate as possible.

==References==

Andrle, M.S., and Crassidis, J.L., “[http://ancs.eng.buffalo.edu/pdf/ancs_papers/2012/geom_int.pdf Geometric Integration of Quaternions],” AIAA/AAS Astrodynamics Specialist Conference, Minneapolis, MN, Aug. 2012, AIAA Paper #2012-4421.

Geometric Integration

2012-11-13T20:20:47Z

Msandrle: /* Findings */

==What is Geometric Integration?==

==Motivation==

[[File:sat_cloud.png|thumbnail|right|150px|Alt=SSA|Space Object Cloud]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Truncation and Roundoff Error Tradeoff]]
In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

For small time-steps (< 1s) the CG geometric approach did not perform as well as classical Runge-Kutta (RK) methods. Geometric approaches require more calculations and in this step-size regime, roundoff error and machine precision dominate.

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]
The results are particularly important for high fidelity attitude dynamics. Most perturbing forces, such as drag, solar radiation pressure, magnetic forces and gravity gradients, are a direct function of attitude. Attitude dynamics are much faster than orbital dynamics and therefore dictate the integration time-step in any simulator. Improvements made using a geometric integration method significantly increased accuracy at larger time-steps. The great computational cost of high fidelity models and long-term propagation intervals (days, weeks or months) is therefore reduced.

*Attribute Estimation
[[File:shape.png|thumbnail|right|300px|Alt=Satellite Shape|Perturbing Forces Depend on Attitude]]
In applications involving satellite attribute estimation, the size, shape, surface materials and attitude may be unknown. Accurate attitude estimate propagation between measurement updates is essential. Falling well within the domain of SSA, satellite attribute estimation suffers from the same challenges: limited sensor time due to over-tasking, infrequent pass opportunities and longer propagation intervals. While sparse data must be used most effectively, attitude propagation between measurement updates must be as accurate as possible.

==References==

Andrle, M.S., and Crassidis, J.L., “[http://ancs.eng.buffalo.edu/pdf/ancs_papers/2012/geom_int.pdf Geometric Integration of Quaternions],” AIAA/AAS Astrodynamics Specialist Conference, Minneapolis, MN, Aug. 2012, AIAA Paper #2012-4421.

Geometric Integration

2012-11-13T20:08:56Z

Msandrle: /* References */

==What is Geometric Integration?==

==Motivation==

[[File:sat_cloud.png|thumbnail|right|150px|Alt=SSA|Space Object Cloud]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Truncation and Roundoff Error Tradeoff]]
In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

The quick brown fox jumps over the lazy dog.<ref name="LazyDog" />

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]
The results are particularly important for high fidelity attitude dynamics. Most perturbing forces, such as drag, solar radiation pressure, magnetic forces and gravity gradients, are a direct function of attitude. Attitude dynamics are much faster than orbital dynamics and therefore dictate the integration time-step in any simulator. Improvements made using a geometric integration method significantly increased accuracy at larger time-steps. The great computational cost of high fidelity models and long-term propagation intervals (days, weeks or months) is therefore reduced.

*Attribute Estimation
[[File:shape.png|thumbnail|right|300px|Alt=Satellite Shape|Perturbing Forces Depend on Attitude]]
In applications involving satellite attribute estimation, the size, shape, surface materials and attitude may be unknown. Accurate attitude estimate propagation between measurement updates is essential. Falling well within the domain of SSA, satellite attribute estimation suffers from the same challenges: limited sensor time due to over-tasking, infrequent pass opportunities and longer propagation intervals. While sparse data must be used most effectively, attitude propagation between measurement updates must be as accurate as possible.

==References==

Andrle, M.S., and Crassidis, J.L., “[http://ancs.eng.buffalo.edu/pdf/ancs_papers/2012/geom_int.pdf Geometric Integration of Quaternions],” AIAA/AAS Astrodynamics Specialist Conference, Minneapolis, MN, Aug. 2012, AIAA Paper #2012-4421.

Geometric Integration

2012-11-13T20:06:07Z

Msandrle:

==What is Geometric Integration?==

==Motivation==

[[File:sat_cloud.png|thumbnail|right|150px|Alt=SSA|Space Object Cloud]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Truncation and Roundoff Error Tradeoff]]
In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

The quick brown fox jumps over the lazy dog.<ref name="LazyDog" />

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]
The results are particularly important for high fidelity attitude dynamics. Most perturbing forces, such as drag, solar radiation pressure, magnetic forces and gravity gradients, are a direct function of attitude. Attitude dynamics are much faster than orbital dynamics and therefore dictate the integration time-step in any simulator. Improvements made using a geometric integration method significantly increased accuracy at larger time-steps. The great computational cost of high fidelity models and long-term propagation intervals (days, weeks or months) is therefore reduced.

*Attribute Estimation
[[File:shape.png|thumbnail|right|300px|Alt=Satellite Shape|Perturbing Forces Depend on Attitude]]
In applications involving satellite attribute estimation, the size, shape, surface materials and attitude may be unknown. Accurate attitude estimate propagation between measurement updates is essential. Falling well within the domain of SSA, satellite attribute estimation suffers from the same challenges: limited sensor time due to over-tasking, infrequent pass opportunities and longer propagation intervals. While sparse data must be used most effectively, attitude propagation between measurement updates must be as accurate as possible.

==References==

<references>
<ref name="LazyDog">This is the lazy dog reference.</ref>
</references>

Andrle, M.S., and Crassidis, J.L., “[http://ancs.eng.buffalo.edu/pdf/ancs_papers/2012/geom_int.pdf Geometric Integration of Quaternions],” AIAA/AAS Astrodynamics Specialist Conference, Minneapolis, MN, Aug. 2012, AIAA Paper #2012-4421.

Geometric Integration

2012-11-13T20:04:05Z

Msandrle: /* References */

==What is Geometric Integration?==

==Motivation==

[[File:sat_cloud.png|thumbnail|right|150px|Alt=SSA|Space Object Cloud]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Truncation and Roundoff Error Tradeoff]]
In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

<ref name="andrle">"John Chrysostom", ''Encyclopaedia Judaica''</ref>

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]
The results are particularly important for high fidelity attitude dynamics. Most perturbing forces, such as drag, solar radiation pressure, magnetic forces and gravity gradients, are a direct function of attitude. Attitude dynamics are much faster than orbital dynamics and therefore dictate the integration time-step in any simulator. Improvements made using a geometric integration method significantly increased accuracy at larger time-steps. The great computational cost of high fidelity models and long-term propagation intervals (days, weeks or months) is therefore reduced.

*Attribute Estimation
[[File:shape.png|thumbnail|right|300px|Alt=Satellite Shape|Perturbing Forces Depend on Attitude]]
In applications involving satellite attribute estimation, the size, shape, surface materials and attitude may be unknown. Accurate attitude estimate propagation between measurement updates is essential. Falling well within the domain of SSA, satellite attribute estimation suffers from the same challenges: limited sensor time due to over-tasking, infrequent pass opportunities and longer propagation intervals. While sparse data must be used most effectively, attitude propagation between measurement updates must be as accurate as possible.

==References==

<references />

{{Reflist}}

Andrle, M.S., and Crassidis, J.L., “[http://ancs.eng.buffalo.edu/pdf/ancs_papers/2012/geom_int.pdf Geometric Integration of Quaternions],” AIAA/AAS Astrodynamics Specialist Conference, Minneapolis, MN, Aug. 2012, AIAA Paper #2012-4421.

Geometric Integration

2012-11-13T19:56:47Z

Msandrle: /* References */

==What is Geometric Integration?==

==Motivation==

[[File:sat_cloud.png|thumbnail|right|150px|Alt=SSA|Space Object Cloud]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Truncation and Roundoff Error Tradeoff]]
In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

<ref name="andrle">"John Chrysostom", ''Encyclopaedia Judaica''</ref>

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]
The results are particularly important for high fidelity attitude dynamics. Most perturbing forces, such as drag, solar radiation pressure, magnetic forces and gravity gradients, are a direct function of attitude. Attitude dynamics are much faster than orbital dynamics and therefore dictate the integration time-step in any simulator. Improvements made using a geometric integration method significantly increased accuracy at larger time-steps. The great computational cost of high fidelity models and long-term propagation intervals (days, weeks or months) is therefore reduced.

*Attribute Estimation
[[File:shape.png|thumbnail|right|300px|Alt=Satellite Shape|Perturbing Forces Depend on Attitude]]
In applications involving satellite attribute estimation, the size, shape, surface materials and attitude may be unknown. Accurate attitude estimate propagation between measurement updates is essential. Falling well within the domain of SSA, satellite attribute estimation suffers from the same challenges: limited sensor time due to over-tasking, infrequent pass opportunities and longer propagation intervals. While sparse data must be used most effectively, attitude propagation between measurement updates must be as accurate as possible.

==References==

{{Reflist}}

Andrle, M.S., and Crassidis, J.L., “[http://ancs.eng.buffalo.edu/pdf/ancs_papers/2012/geom_int.pdf Geometric Integration of Quaternions],” AIAA/AAS Astrodynamics Specialist Conference, Minneapolis, MN, Aug. 2012, AIAA Paper #2012-4421.

Geometric Integration

2012-11-13T19:52:57Z

Msandrle: /* References */

==What is Geometric Integration?==

==Motivation==

[[File:sat_cloud.png|thumbnail|right|150px|Alt=SSA|Space Object Cloud]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Truncation and Roundoff Error Tradeoff]]
In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

<ref name="andrle">"John Chrysostom", ''Encyclopaedia Judaica''</ref>

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]
The results are particularly important for high fidelity attitude dynamics. Most perturbing forces, such as drag, solar radiation pressure, magnetic forces and gravity gradients, are a direct function of attitude. Attitude dynamics are much faster than orbital dynamics and therefore dictate the integration time-step in any simulator. Improvements made using a geometric integration method significantly increased accuracy at larger time-steps. The great computational cost of high fidelity models and long-term propagation intervals (days, weeks or months) is therefore reduced.

*Attribute Estimation
[[File:shape.png|thumbnail|right|300px|Alt=Satellite Shape|Perturbing Forces Depend on Attitude]]
In applications involving satellite attribute estimation, the size, shape, surface materials and attitude may be unknown. Accurate attitude estimate propagation between measurement updates is essential. Falling well within the domain of SSA, satellite attribute estimation suffers from the same challenges: limited sensor time due to over-tasking, infrequent pass opportunities and longer propagation intervals. While sparse data must be used most effectively, attitude propagation between measurement updates must be as accurate as possible.

==References==

{{reflist}}

Andrle, M.S., and Crassidis, J.L., “[http://ancs.eng.buffalo.edu/pdf/ancs_papers/2012/geom_int.pdf Geometric Integration of Quaternions],” AIAA/AAS Astrodynamics Specialist Conference, Minneapolis, MN, Aug. 2012, AIAA Paper #2012-4421.

Geometric Integration

2012-11-13T19:51:38Z

Msandrle: /* Findings */

==What is Geometric Integration?==

==Motivation==

[[File:sat_cloud.png|thumbnail|right|150px|Alt=SSA|Space Object Cloud]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Truncation and Roundoff Error Tradeoff]]
In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

<ref name="andrle">"John Chrysostom", ''Encyclopaedia Judaica''</ref>

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]
The results are particularly important for high fidelity attitude dynamics. Most perturbing forces, such as drag, solar radiation pressure, magnetic forces and gravity gradients, are a direct function of attitude. Attitude dynamics are much faster than orbital dynamics and therefore dictate the integration time-step in any simulator. Improvements made using a geometric integration method significantly increased accuracy at larger time-steps. The great computational cost of high fidelity models and long-term propagation intervals (days, weeks or months) is therefore reduced.

*Attribute Estimation
[[File:shape.png|thumbnail|right|300px|Alt=Satellite Shape|Perturbing Forces Depend on Attitude]]
In applications involving satellite attribute estimation, the size, shape, surface materials and attitude may be unknown. Accurate attitude estimate propagation between measurement updates is essential. Falling well within the domain of SSA, satellite attribute estimation suffers from the same challenges: limited sensor time due to over-tasking, infrequent pass opportunities and longer propagation intervals. While sparse data must be used most effectively, attitude propagation between measurement updates must be as accurate as possible.

==References==
Andrle, M.S., and Crassidis, J.L., “[http://ancs.eng.buffalo.edu/pdf/ancs_papers/2012/geom_int.pdf Geometric Integration of Quaternions],” AIAA/AAS Astrodynamics Specialist Conference, Minneapolis, MN, Aug. 2012, AIAA Paper #2012-4421.

Geometric Integration

2012-11-13T19:48:17Z

Msandrle: /* Findings */

==What is Geometric Integration?==

==Motivation==

[[File:sat_cloud.png|thumbnail|right|150px|Alt=SSA|Space Object Cloud]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Truncation and Roundoff Error Tradeoff]]
In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

Blah<ref>Reference 1</ref>

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]
The results are particularly important for high fidelity attitude dynamics. Most perturbing forces, such as drag, solar radiation pressure, magnetic forces and gravity gradients, are a direct function of attitude. Attitude dynamics are much faster than orbital dynamics and therefore dictate the integration time-step in any simulator. Improvements made using a geometric integration method significantly increased accuracy at larger time-steps. The great computational cost of high fidelity models and long-term propagation intervals (days, weeks or months) is therefore reduced.

*Attribute Estimation
[[File:shape.png|thumbnail|right|300px|Alt=Satellite Shape|Perturbing Forces Depend on Attitude]]
In applications involving satellite attribute estimation, the size, shape, surface materials and attitude may be unknown. Accurate attitude estimate propagation between measurement updates is essential. Falling well within the domain of SSA, satellite attribute estimation suffers from the same challenges: limited sensor time due to over-tasking, infrequent pass opportunities and longer propagation intervals. While sparse data must be used most effectively, attitude propagation between measurement updates must be as accurate as possible.

==References==
Andrle, M.S., and Crassidis, J.L., “[http://ancs.eng.buffalo.edu/pdf/ancs_papers/2012/geom_int.pdf Geometric Integration of Quaternions],” AIAA/AAS Astrodynamics Specialist Conference, Minneapolis, MN, Aug. 2012, AIAA Paper #2012-4421.

Geometric Integration

2012-11-13T19:25:53Z

Msandrle: /* Applications */

==What is Geometric Integration?==

==Motivation==

[[File:sat_cloud.png|thumbnail|right|150px|Alt=SSA|Space Object Cloud]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Truncation and Roundoff Error Tradeoff]]
In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]
The results are particularly important for high fidelity attitude dynamics. Most perturbing forces, such as drag, solar radiation pressure, magnetic forces and gravity gradients, are a direct function of attitude. Attitude dynamics are much faster than orbital dynamics and therefore dictate the integration time-step in any simulator. Improvements made using a geometric integration method significantly increased accuracy at larger time-steps. The great computational cost of high fidelity models and long-term propagation intervals (days, weeks or months) is therefore reduced.

*Attribute Estimation
[[File:shape.png|thumbnail|right|300px|Alt=Satellite Shape|Perturbing Forces Depend on Attitude]]
In applications involving satellite attribute estimation, the size, shape, surface materials and attitude may be unknown. Accurate attitude estimate propagation between measurement updates is essential. Falling well within the domain of SSA, satellite attribute estimation suffers from the same challenges: limited sensor time due to over-tasking, infrequent pass opportunities and longer propagation intervals. While sparse data must be used most effectively, attitude propagation between measurement updates must be as accurate as possible.

Geometric Integration

2012-11-13T19:14:38Z

Msandrle: /* Applications */

==What is Geometric Integration?==

==Motivation==

[[File:sat_cloud.png|thumbnail|right|150px|Alt=SSA|Space Object Cloud]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Truncation and Roundoff Error Tradeoff]]
In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]
The results are particularly important for high fidelity attitude dynamics. Most perturbing forces, such as drag, solar radiation pressure, magnetic forces and gravity gradients, are a direct function of attitude. Attitude dynamics are much faster than orbital dynamics and therefore dictate the integration time-step in any simulator. Improvements made using a geometric integration method significantly increased accuracy at larger time-steps. The great computational cost of high fidelity models and long-term propagation intervals (days, weeks or months) is therefore reduced.

*Attribute Estimation
[[File:shape.png|thumbnail|right|300px|Alt=Satellite Shape|Perturbing Forces Depend on Attitude]]
In applications involving satellite attribute estimation, the size, shape, surface materials and attitude may be unknown. Accurate attitude estimate propagation between measurement updates is essential.

Geometric Integration

2012-11-13T19:07:25Z

Msandrle: /* Applications */

==What is Geometric Integration?==

==Motivation==

[[File:sat_cloud.png|thumbnail|right|150px|Alt=SSA|Space Object Cloud]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Truncation and Roundoff Error Tradeoff]]
In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]
The results are particularly important for high fidelity attitude dynamics. Most perturbing forces, such as drag, solar radiation pressure, magnetic forces and gravity gradients, are a direct function of attitude. Attitude dynamics are much faster than orbital dynamics and therefore dictate the integration time-step in any simulator. Improvements made using a geometric integration method significantly increased accuracy at larger time-steps. The great computational cost of high fidelity models and long-term propagation intervals (days, weeks or months) is therefore reduced.

*Attribute Estimation
[[File:shape.png|thumbnail|right|300px|Alt=Satellite Shape|Perturbing Forces Depend on Attitude]]
In applications involving satellite attribute estimation, the size, shape, surface materials and attitude may be unknown. Accurate attitude estimate propagation between measurement updates is

Geometric Integration

2012-11-13T19:01:40Z

Msandrle:

Geometric Integration

2012-11-13T18:59:15Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T18:57:49Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T18:57:25Z

Msandrle: /* Motivation */

File:Shape.png

2012-11-13T18:56:25Z

Msandrle: Illustrates perturbing force-attitude relationship. Good for shape estimation.

Illustrates perturbing force-attitude relationship. Good for shape estimation.

Geometric Integration

2012-11-13T18:53:50Z

Msandrle: /* Applications */

Geometric Integration

2012-11-13T18:45:45Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T18:43:24Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T18:42:56Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T18:42:32Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T18:42:16Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T18:38:54Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T18:38:24Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T18:37:07Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T18:34:33Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T18:34:11Z

Msandrle: /* Motivation */

==What is Geometric Integration?==

==Motivation==

[[File:sat_cloud.png|thumbnail|right|150px|Alt=SSA|Caption Space Object Cloud]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Caption Truncation and Roundoff Error Tradeoff]]

In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]

*Shape Estimation

Geometric Integration

2012-11-13T18:33:01Z

Msandrle: /* Motivation */

==What is Geometric Integration?==

==Motivation==

[[File:sat_cloud.png|thumbnail|right|150px|Alt=Error Tradeoff|Caption Truncation and Roundoff Error Tradeoff]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Caption Truncation and Roundoff Error Tradeoff]]

In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]

*Shape Estimation

Geometric Integration

2012-11-13T18:32:21Z

Msandrle: /* Motivation */

==What is Geometric Integration?==

==Motivation==

[[File:sat_cloud.png|thumbnail|right|Alt=Error Tradeoff|Caption Truncation and Roundoff Error Tradeoff]]One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Caption Truncation and Roundoff Error Tradeoff]]

In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]

*Shape Estimation

File:Sat cloud.png

2012-11-13T18:31:23Z

Msandrle: Satellite cloud good for SSA and projects motivated by SSA.

Satellite cloud good for SSA and projects motivated by SSA.

Geometric Integration

2012-11-13T17:41:48Z

Msandrle: /* Motivation */

==What is Geometric Integration?==

==Motivation==

One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|left|400px|Alt=Error Tradeoff|Caption Truncation and Roundoff Error Tradeoff]]

In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]

*Shape Estimation

Geometric Integration

2012-11-13T17:39:56Z

Msandrle: /* Motivation */

==What is Geometric Integration?==

==Motivation==

One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|right|400px|Alt=Error Tradeoff|Caption Truncation and Roundoff Error Tradeoff]]

In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]

*Shape Estimation

Geometric Integration

2012-11-13T17:27:16Z

Msandrle:

==What is Geometric Integration?==

==Motivation==

One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|400px|Alt=Error Tradeoff|Caption Truncation and Roundoff Error Tradeoff]]

In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Findings==

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]

*Shape Estimation

Geometric Integration

2012-11-13T17:21:58Z

Msandrle: /* Motivation */

==What is Geometric Integration?==

==Motivation==

One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|400px|Alt=Error Tradeoff|Caption Truncation and Roundoff Error Tradeoff]]

In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

'''Objective''': Employ a Lie group method developed by Crouch and Grossman for the numerical integration of a quaternion parameterized attitude. Its inherent ability to integrate along the unit hypersphere helps to minimize truncation errors while maintaining the quaternion unit norm constraint.

==Approach==

==Findings==

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]

*Shape Estimation

Geometric Integration

2012-11-13T17:16:41Z

Msandrle: /* Motivation */

==What is Geometric Integration?==

==Motivation==

One prevailing field in which this research is very pertinent is [[Space_Situational_Awareness|Space Situational Awareness]] (SSA). The U.S. Air Force is collecting data in order to catalogue Earth orbiting satellites, objects and debris. Accurate tracking of all Earth-orbiting objects is important since their presence can be extremely hazardous to current space missions as well as disruptive for any future mission. This danger can either be passive in the case of debris or active intentional threats. However, the data necessary to estimate the attitude of such objects is sparse as a result of the number of sensors available and the large number of objects in orbit. In particular, for objects in low-Earth orbit (LEO), observation windows may last only a few minutes and opportunities to obtain data are infrequent. As a result, an object's attitude may often need to be propagated very accurately over long time intervals before an update is possible.[[File:step_size.png|thumbnail|400px|Alt=Error Tradeoff|Caption Truncation and Roundoff Error Tradeoff]]

In summary, the problem is that
*there are an increasing number of space objects to track
*sensor time per object is limited
*many objects yield infrequent pass opportunities

Advanced estimation techniques require accurate integration during extended propagation intervals when limited observation data is available.

==Approach==

==Findings==

==Applications==

*[[High_Fidelity_Orbit_Simulation|High Fidelity Orbit Simulation]]

*Shape Estimation

Geometric Integration

2012-11-13T17:12:03Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T17:11:47Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T17:11:36Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T17:10:42Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T17:10:28Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T17:09:50Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T17:07:54Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T17:07:17Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T17:06:36Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T17:05:21Z

Msandrle: /* Motivation */

Geometric Integration

2012-11-13T17:04:27Z

Msandrle: /* Motivation */