Best Linear Unbiased Minimum-Variance Estimator (BLUE) - Revision history

Carlos.Lopez at 11:20, 23 February 2012

2012-02-23T11:20:48Z

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	\|YearOfPublication=2011		\|YearOfPublication=2011
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Jaume.Sanz at 12:30, 23 January 2012

2012-01-23T12:30:30Z

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	(see for instance [RTCA-MOPS, 2006] <ref>[RTCA-MOPS, 2006] RTCA-MOPS, 2006. Minimum Operational Performance Standards for Global Positioning System / Wide Area Augmentation System Airborne Equipment.rtca document 229-c.</ref>), where UERE stands for the User Equivalent Range Error. A ~~simplest~~ model for (7) is to take just an elevation dependent function according to the expression:		(see for instance [RTCA-MOPS, 2006] <ref>[RTCA-MOPS, 2006] RTCA-MOPS, 2006. Minimum Operational Performance Standards for Global Positioning System / Wide Area Augmentation System Airborne Equipment.rtca document 229-c.</ref>), where UERE stands for the User Equivalent Range Error. A simpler model for (7) is to take just an elevation dependent function according to the expression:

	:<math>		:<math>

Jaume.Sanz at 12:26, 23 January 2012

2012-01-23T12:26:39Z

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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=Advanced		\|Level=Advanced
	\|YearOfPublication=2011		\|YearOfPublication=2011
	\|Logo=gAGE		\|Logo=gAGE
			\|Title={{PAGENAME}}
	}}		}}

	The weighting matrix <math>{\mathbf W}</math> of the [[Weighted Least Square Solution (WLS)\| Weighted Least Square solution (WLS)]] is a way to account for the different quality of the data in the adjustment problem. In this article, it is introduced the criteria to define this matrix.		The weighting matrix <math>{\mathbf W}</math> of the [[Weighted Least Square Solution (WLS)\| Weighted Least Square solution (WLS)]] is a way to account for the different quality of the data in the adjustment problem. In this article, it is introduced the criteria to define this matrix.

Carlos.Lopez at 09:58, 28 October 2011

2011-10-28T09:58:23Z

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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=Advanced
	\|YearOfPublication=2011		\|YearOfPublication=2011
	\|Logo=gAGE		\|Logo=gAGE

Carlos.Lopez at 08:10, 9 August 2011

2011-08-09T08:10:33Z

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	[[Category:Fundamentals]]		[[Category:Fundamentals]]
			[[Category:Solving Navigation Equations]]

Carlos.Lopez at 13:26, 5 August 2011

2011-08-05T13:26:27Z

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	The weighting matrix <math>{\mathbf W}</math> of the [[Weighted Least Square Solution (WLS)\| Weighted Least Square solution (WLS )]] is a way to account for the different quality of the data in the adjustment problem. In this article, it is introduced the criteria to define this matrix.		The weighting matrix <math>{\mathbf W}</math> of the [[Weighted Least Square Solution (WLS)\| Weighted Least Square solution (WLS)]] is a way to account for the different quality of the data in the adjustment problem. In this article, it is introduced the criteria to define this matrix.

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..."

2011-07-25T07:41:13Z

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 weighting matrix <math>{\mathbf W}</math> of the [[Weighted Least Square Solution (WLS)| Weighted Least Square solution (WLS )]] is a way to account for the different quality of the data in the adjustment problem. In this article, it is introduced the criteria to define this matrix.

The equations (1) and (2) (see [[Weighted Least Square Solution (WLS)| Weighted Least Square solution (WLS)]])

:<math>
\hat{\mathbf X}_{_{\mathbf W}}=({\mathbf G}^T\,{\mathbf W}\,{\mathbf G})^{-1}{\mathbf G}^T\,{\mathbf W}\,{\mathbf Y} \qquad \mbox{(1)}
</math>

:<math>
{\mathbf P}_{_{\mathbf \Delta X_W}}=({\mathbf G}^T\,{\mathbf W}\,{\mathbf G})^{-1}{\mathbf G}^T \,{\mathbf W}\,\,{\mathbf R}\,\,{\mathbf W}\,{\mathbf G}({\mathbf G}^T\,{\mathbf W}\,{\mathbf G})^{-1} \qquad \mbox{(2)}
</math>

are simplified when taking the weighting matrix <math>{\mathbf W}</math> as the inverse of covariance matrix <math>{\mathbf R}</math>. That is, when:

:<math>
{\mathbf W}= {\mathbf R}^{-1}
\qquad \mbox{(3)}
</math>

thence, the equations (1) and (2) become

:<math>
\hat{\mathbf X}=({\mathbf G}^T\,{\mathbf R}^{-1}\,{\mathbf G})^{-1}{\mathbf G}^T\,{\mathbf R^{-1}}\,{\mathbf Y}
\qquad \mbox{(4)}
</math>

:<math>
{\mathbf P}=({\mathbf G}^T\,{\mathbf R}^{-1}\,{\mathbf G})^{-1}
\qquad \mbox{(5)}
</math>

From a different approach, it can be shown (see for instance [Tapley et al., 2004] <ref> [Tapley et al., 2004] Tapley, B., Schutz, B. and Born, G., 2004. Statistical Orbit Determination. Academic Press, Inc., Amsterdam, The Netherlands.Ta:04:ORB </ref> or [Bierman, 1976] <ref> [Bierman, 1976] Bierman, G., 1976. Factorization Methods fro Discrete Sequential estimation. Academic Press, New York, New York, USA.</ref> ) that this solution corresponds to the '''Best Linear Unbiased Minimum-Variance Estimator'''.

The '''minimum variance criteria''' is widely used because its simplicity. It has the advantage that the complete statistical description of the random errors is not required. Rather, only the first and second order statics for the measurement error are needed <ref group="footnotes">The minimum variance estimate (4) gives the maximum likelihood estimate, when the observation errors are assumed to be distributed normally with zero mean and covariance <math>{\mathbf R}</math>.</ref> (i.e., <math>E[{\boldsymbol \varepsilon}]=0\; ;\; {\mathbf R}=E[{\boldsymbol \varepsilon} \, {\boldsymbol \varepsilon}^T]</math>).

Unfortunately, the characterisation of measurement error is very difficult and even the covariance matrix is not usually known. A simplification commonly used is to assume that the measurements (prefit-residuals <math>{\mathbf Y}=\left( y_1, ... , y_n \right)^T</math>) from the different satellites are uncorrelated. Thence, the weighting matrix <math>{\mathbf W}</math> becomes:

:<math>
W={\mathbf R}^{-1}=\left(
{\begin{array}{ccc}
1/\sigma^2_{y_ {1}} & & \\
& \ddots & \\
& & 1/\sigma^2_{y{_n}}
\end{array}
}
\right) \qquad \mbox{(6)}
</math>

where <math>\sigma^2_{y_{i}}</math> comes from the uncertainty of the different error sources (satellite clocks, ephemeris, ionosphere, troposphere, multipath and receiver noise):

:<math>
\sigma^2_{y_i}\equiv\sigma^2_{_{UERE_i}}=\sigma^2_{clk_i}+\sigma^2_{eph_i}+\sigma^2_{iono_i}+\sigma^2_{tropo_i}+\sigma^2_{mp_i}+\sigma^2_{noise_i}
\qquad \mbox{(7)}
</math>

(see for instance [RTCA-MOPS, 2006] <ref>[RTCA-MOPS, 2006] RTCA-MOPS, 2006. Minimum Operational Performance Standards for Global Positioning System / Wide Area Augmentation System Airborne Equipment.rtca document 229-c.</ref>), where UERE stands for the User Equivalent Range Error. A simplest model for (7) is to take just an elevation dependent function according to the expression:

:<math>
\sigma_{y_i}=a+b\,e^{-elev/c}
\qquad \mbox{(8)}
</math>

==Notes==
<references group="footnotes"/>

==References==
<references/>

[[Category:Fundamentals]]