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

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Appendix I / Utilities / Covariance<br />

_ _<br />

y : = EIGEN_METRIC<br />

( i , j ) ∈ [ 1(<br />

1 ) n ]<br />

C<br />

C N<br />

⇒<br />

22.13-13<br />

ϕ<br />

: =<br />

: = n ⋅ ϕ trace<br />

max<br />

( [ C ] , n , k )<br />

C<br />

Equation 22.13-66<br />

C(<br />

i , j )<br />

( i , i ) ⋅ C(<br />

j , j )<br />

( ϕ )<br />

( C ) ( C )<br />

N < 30 ⇒ y : = λ λ<br />

N C<br />

≥<br />

30<br />

⇒<br />

y<br />

N<br />

: =<br />

0<br />

min<br />

Equation 22.13-67<br />

Equation 22.13-68<br />

N<br />

Equation 22.13-69<br />

Equation 22.13-70<br />

( λMAX , λMIN ) are the maximum and minimum eigenvalues. (NC) is the<br />

number of iterations taken by the EIGEN to complete the annihilation of the<br />

off-diagonal terms in the covariance matrix. Care should be exercised if the<br />

number of iterations required for annihilation ( k := 30 ).<br />

22.13.15 Covariance Tracking Metric<br />

STATE_METRIC takes covariance matrix [C] of rank (n), the estimated and<br />

actual state vectors, returning the Chi-squared tracking accuracy metric.<br />

k<br />

y : = STATE_METRIC<br />

k<br />

1<br />

=<br />

0<br />

⇒<br />

⇒<br />

: =<br />

22.13.16 Mahalanobis Distance Metric<br />

=<br />

y<br />

y<br />

: =<br />

( X , X , [ C ] , n , k )<br />

ˆ<br />

⎛<br />

⎜ X − ˆ<br />

( i ) X ( i )<br />

C(<br />

i , )<br />

∏ ⎟<br />

= ⎟ ⎟<br />

n<br />

⎜<br />

i : 1 ⎜ i<br />

⎝<br />

( ) [ ] ( X X ) ˆ<br />

X X C<br />

ˆ<br />

T −1<br />

− ⋅ ⋅ −<br />

Equation 22.13-71<br />

⎞<br />

⎠<br />

Equation 22.13-72<br />

Equation 22.13-73<br />

FF_D_METRIC takes the measurement residual (n)-vector (Z), and the<br />

expected measurement uncertainty matrix [A], terms defined in §4.4, and<br />

returns the Mahalanobis distance metric, Pao [P.8] ,

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