Parameters adjustment for PPP - Revision history

Edo.Tzumer at 11:37, 17 January 2016

2016-01-17T11:37:27Z

← Older revision		Revision as of 11:37, 17 January 2016
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	::* '''Receiver clock''' (<math>c\, \delta t</math>) is taken as a white noise process (with <math>\sigma= 3\, 10^5\; m</math>, i.e, <math>1</math> millisecond, for instance.), see [[Kalman Filter}]].		::* '''Receiver clock''' (<math>c\, \delta t</math>) is taken as a white noise process (with <math>\sigma= 3\, 10^5\; m</math>, i.e, <math>1</math> millisecond, for instance.), see [[Kalman Filter]].

Jaume.Sanz at 10:28, 13 January 2013

2013-01-13T10:28:03Z

← Older revision		Revision as of 10:28, 13 January 2013
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	The following stochastic model can be used for the filter:		The following stochastic model can be used for the filter:

	::* '''Carrier phase biases''' (<math>B_C</math>) are taken as "constants" along continuous phase arcs, and "white-noise" when a cycle-slip happens (with <math>\sigma=10^4\,m</math>, for instance~~, after a previous carrier-code alignment~~), see [[Kalman Filter]].		::* '''Carrier phase biases''' (<math>B_C</math>) are taken as "constants" along continuous phase arcs, and "white-noise" when a cycle-slip happens (with <math>\sigma=10^4\,m</math>, for instance), see [[Kalman Filter]].

Carlos.Lopez at 11:34, 23 February 2012

2012-02-23T11:34:54Z

← Older revision		Revision as of 11:34, 23 February 2012
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	\|Level=Basic		\|Level=Basic
	\|YearOfPublication=2011		\|YearOfPublication=2011
	~~\|Logo=gAGE~~
	\|Title={{PAGENAME}}		\|Title={{PAGENAME}}
	}}		}}

Jaume.Sanz at 16:58, 25 January 2012

2012-01-25T16:58:29Z

← Older revision		Revision as of 16:58, 25 January 2012
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	\|Title={{PAGENAME}}		\|Title={{PAGENAME}}
	}}		}}
	The linear observation model <math>{\mathbf Y}={\mathbf G}\;{\mathbf X}</math> can be solved using [[Kalman Filter]], considering the carrier phase bias <math>B^i</math> as "constants" along continuous phase arcs, and ''white-noise'' at the instants when cycle-slips occurs.		The linear observation model <math>{\mathbf Y}={\mathbf G}\;{\mathbf X}</math> can be solved using [[Kalman Filter]], considering the carrier phase bias <math>B_{C}^i</math> as "constants" along continuous phase arcs, and ''white-noise'' at the instants when cycle-slips occurs.

Jaume.Sanz at 16:56, 25 January 2012

2012-01-25T16:56:08Z

← Older revision		Revision as of 16:56, 25 January 2012
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	{{Article Infobox2		{{Article Infobox2
	\|Category=Fundamentals		\|Category=Fundamentals
	~~\|Title={{PAGENAME}}~~		\|Authors=J. Sanz Subirana, J.M. Juan Zornoza and M. Hernández-Pajares, Technical University of Catalonia, Spain.
	\|Authors= J. Sanz Subirana, JM. Juan Zornoza and M. ~~Hernandez~~-Pajares, University of ~~Catalunia~~, Spain.
	\|Level=Basic		\|Level=Basic
	\|YearOfPublication=2011		\|YearOfPublication=2011
	\|Logo=gAGE		\|Logo=gAGE
			\|Title={{PAGENAME}}
	}}		}}

	The linear observation model <math>{\mathbf Y}={\mathbf G}\;{\mathbf X}</math> can be solved using [[Kalman Filter]], considering the carrier phase bias <math>B^i</math> as "constants" along continuous phase arcs, and ''white-noise'' at the instants when cycle-slips occurs.		The linear observation model <math>{\mathbf Y}={\mathbf G}\;{\mathbf X}</math> can be solved using [[Kalman Filter]], considering the carrier phase bias <math>B^i</math> as "constants" along continuous phase arcs, and ''white-noise'' at the instants when cycle-slips occurs.

Carlos.Lopez at 10:03, 28 October 2011

2011-10-28T10:03:53Z

← Older revision		Revision as of 10:03, 28 October 2011
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	\|Title={{PAGENAME}}		\|Title={{PAGENAME}}
	\|Authors= J. Sanz Subirana, JM. Juan Zornoza and M. Hernandez-Pajares, University of Catalunia, Spain.		\|Authors= J. Sanz Subirana, JM. Juan Zornoza and M. Hernandez-Pajares, University of Catalunia, Spain.
	\|Level=~~Medium~~		\|Level=Basic
	\|YearOfPublication=2011		\|YearOfPublication=2011
	\|Logo=gAGE		\|Logo=gAGE

Carlos.Lopez at 10:03, 28 October 2011

2011-10-28T10:03:40Z

← Older revision		Revision as of 10:03, 28 October 2011
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	::* '''Wet tropospheric delay''' (<math>\Delta T_{z,wet}</math>) is taken as a random-walk process (a process noise with <math>d\sigma^2/dt= 1\,cm^2/h</math>, initialised with <math>\sigma^2_0=0.25\, m^2</math>, can be used for most of the applications), see [[Kalman Filter}]].		::* '''Wet tropospheric delay''' (<math>\Delta T_{z,wet}</math>) is taken as a random-walk process (a process noise with <math>d\sigma^2/dt= 1\,cm^2/h</math>, initialised with <math>\sigma^2_0=0.25\, m^2</math>, can be used for most of the applications), see [[Kalman Filter]].

Carlos.Lopez at 08:15, 9 August 2011

2011-08-09T08:15:21Z

← Older revision		Revision as of 08:15, 9 August 2011
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	[[Category:Fundamentals]]		[[Category:Fundamentals]]
			[[Category:Solving Navigation Equations]]
			[[Category:PPP]]

Carlos.Lopez at 14:02, 5 August 2011

2011-08-05T14:02:11Z

← Older revision		Revision as of 14:02, 5 August 2011
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	}}		}}

	The linear observation model <math>{\mathbf Y}={\mathbf G}\;{\mathbf X}</math> can be solved using [[Kalman Filter]], considering the carrier phase bias <math>B^i</math> as "constants" along continuous phase arcs, and "white-noise" at the instants when cycle-slips occurs.		The linear observation model <math>{\mathbf Y}={\mathbf G}\;{\mathbf X}</math> can be solved using [[Kalman Filter]], considering the carrier phase bias <math>B^i</math> as "constants" along continuous phase arcs, and ''white-noise'' at the instants when cycle-slips occurs.


	The following stochastic model can be used for the filter:		The following stochastic model can be used for the filter:

	::* '''Carrier phase biases''' (<math>B_C</math>) are taken as "constants" along continuous phase arcs, and "white-noise" when a cycle-slip happens (with <math>\sigma=10^4\,m</math>, for instance, after a previous carrier-code alignment), see [[Kalman Filter}]].		::* '''Carrier phase biases''' (<math>B_C</math>) are taken as "constants" along continuous phase arcs, and "white-noise" when a cycle-slip happens (with <math>\sigma=10^4\,m</math>, for instance, after a previous carrier-code alignment), see [[Kalman Filter]].


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	This solving procedure is called ~~{\em~~ to float} ambiguities. Floating in the sense that they are estimated by the filter "as real numbers". The bias estimations <math>B^i</math> will converge into a solution after a transition time that depends on the observation geometry, model quality and data noise. In general, one must expect errors at the decimetre level in pure kinematic positioning (i.e., coordinates <math>(x,y,z)</math> modelled as white-noise) and at the centimetre level in static PPP.		This solving procedure is called ''to float'' ambiguities. Floating in the sense that they are estimated by the filter "as real numbers". The bias estimations <math>B^i</math> will converge into a solution after a transition time that depends on the observation geometry, model quality and data noise. In general, one must expect errors at the decimetre level in pure kinematic positioning (i.e., coordinates <math>(x,y,z)</math> modelled as white-noise) and at the centimetre level in static PPP.



	[[Category:Fundamentals]]		[[Category:Fundamentals]]

Carlos.Lopez: Created page with "{{Article Infobox2 |Category=Fundamentals |Title={{PAGENAME}} |Authors= J. Sanz Subirana, JM. Juan Zornoza and M. Hernandez-Pajares, University of Catalunia, Spain. |Level=Medium..."

2011-07-22T14:11:06Z

Created page with "{{Article Infobox2 |Category=Fundamentals |Title={{PAGENAME}} |Authors= J. Sanz Subirana, JM. Juan Zornoza and M. Hernandez-Pajares, University of Catalunia, Spain. |Level=Medium..."

New page

{{Article Infobox2
|Category=Fundamentals
|Title={{PAGENAME}}
|Authors= J. Sanz Subirana, JM. Juan Zornoza and M. Hernandez-Pajares, University of Catalunia, Spain.
|Level=Medium
|YearOfPublication=2011
|Logo=gAGE
}}

The linear observation model <math>{\mathbf Y}={\mathbf G}\;{\mathbf X}</math> can be solved using [[Kalman Filter]], considering the carrier phase bias <math>B^i</math> as "constants" along continuous phase arcs, and "white-noise" at the instants when cycle-slips occurs.

The following stochastic model can be used for the filter:

::* '''Carrier phase biases''' (<math>B_C</math>) are taken as "constants" along continuous phase arcs, and "white-noise" when a cycle-slip happens (with <math>\sigma=10^4\,m</math>, for instance, after a previous carrier-code alignment), see [[Kalman Filter}]].

::* '''Wet tropospheric delay''' (<math>\Delta T_{z,wet}</math>) is taken as a random-walk process (a process noise with <math>d\sigma^2/dt= 1\,cm^2/h</math>, initialised with <math>\sigma^2_0=0.25\, m^2</math>, can be used for most of the applications), see [[Kalman Filter}]].

::* '''Receiver clock''' (<math>c\, \delta t</math>) is taken as a white noise process (with <math>\sigma= 3\, 10^5\; m</math>, i.e, <math>1</math> millisecond, for instance.), see [[Kalman Filter}]].

::* '''Receiver coordinates''' (<math>dx,dy,dz</math>)

::: For static positioning: the coordinates are taken as constants, see [[Kalman Filter]].
::: For kinematic positioning: the coordinates are taken as white noise or a random walk process as in [[Kalman Filter]].

This solving procedure is called {\em to float} ambiguities. Floating in the sense that they are estimated by the filter "as real numbers". The bias estimations <math>B^i</math> will converge into a solution after a transition time that depends on the observation geometry, model quality and data noise. In general, one must expect errors at the decimetre level in pure kinematic positioning (i.e., coordinates <math>(x,y,z)</math> modelled as white-noise) and at the centimetre level in static PPP.

[[Category:Fundamentals]]