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Thesis - Leigh Moody.pdf - Bad Request - Cranfield University

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Glossary<br />

_ _<br />

transformation of covariance matrices, we have [M C ]. Providing that<br />

matrices are clear in context the square bracket notation may be omitted.<br />

0.16.2 Diagonalisation<br />

When converting a vector into a diagonal matrix (in this case for a position<br />

3-vector),<br />

C<br />

XC YC ZC<br />

( P ) diag ( P , P , P )<br />

diag ≡<br />

diag<br />

a,<br />

b<br />

C ( P )<br />

a,<br />

b<br />

≡<br />

⎡ P<br />

⎢<br />

⎢<br />

⎢ 0<br />

⎢<br />

⎢<br />

⎢<br />

⎣ 0<br />

xl<br />

XC<br />

a,<br />

b<br />

The trace of a square matrix of dimension (N) is the sum of its diagonal<br />

elements,<br />

0.16.3 Special Matrices<br />

tr<br />

a,<br />

b<br />

,<br />

,<br />

,<br />

N<br />

∑<br />

i : = 1<br />

P<br />

0<br />

YC<br />

a,<br />

b<br />

0<br />

a,<br />

b<br />

[ A ] : = a ( i , i )<br />

NxN<br />

The identity matrix is written as (in this example, for a matrix of dimension<br />

N := 3),<br />

[ I ]<br />

I 3 ≡ 3<br />

≡<br />

⎡ 1<br />

⎢<br />

⎢<br />

⎢ 0<br />

⎢<br />

⎢<br />

⎢<br />

⎣ 0<br />

,<br />

,<br />

,<br />

0<br />

1<br />

0<br />

,<br />

,<br />

,<br />

,<br />

,<br />

,<br />

P<br />

0<br />

0<br />

ZC<br />

a,<br />

b<br />

0 ⎤<br />

⎥<br />

⎥<br />

0 ⎥<br />

⎥<br />

⎥<br />

1 ⎥<br />

⎦<br />

Zero matrices are written (in this case for a (2,3) matrix),<br />

≡<br />

[ 0 ]<br />

0 2 x3<br />

2x3<br />

≡<br />

⎡ 0<br />

⎢<br />

⎢<br />

⎢⎣<br />

0<br />

,<br />

,<br />

0<br />

0<br />

,<br />

,<br />

a,<br />

b<br />

0 ⎤<br />

⎥<br />

⎥<br />

0 ⎥⎦<br />

The dimensions may be omitted if they are obvious in context<br />

⎤<br />

⎥<br />

⎥<br />

⎥<br />

⎥<br />

⎥<br />

⎥<br />

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