Columns of the identity matrix are added as column vectors for these variables.
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This is equivalent to saying that A times its transpose must be the identity matrix.
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The covariance matrix of T will be the identity matrix.
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This is a consequence of the Gauss-Markov theorem when the conditional variance of the outcome is not scalable to the identity matrix.
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This computation is in accordance with Chapter 11 of The global positioning system by Parkinson and Spilker where the weighting matrix, P, has been set to the identity matrix.
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Assuming the pseudorange errors are uncorrelated and have the same variance, the covariance matrix on the right side can be expressed as a scalar times the identity matrix.
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The variables corresponding to the columns of the identity matrix are called basic variables while the remaining variables are called nonbasic or free variables.
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The variable for this column is now a basic variable, replacing the variable which corresponded to the r-th column of the identity matrix before the operation.
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Though this representation of complex numbers with matricies is the most common, many other representations arise from matrices other than that square to the negative of the identity matrix.