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Identification of atypical flight patterns

  • US 6,937,924 B1
  • Filed: 05/21/2004
  • Issued: 08/30/2005
  • Est. Priority Date: 05/21/2004
  • Status: Expired due to Fees
First Claim
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1. A method for analyzing aircraft flight data, the method comprising:

  • (i) receiving flight data for measurements of each of P selected parameters {m(t;

    k;

    q)} (k=1, . . . , P) at each of N selected times (t=tn) (n=n0, . . . , n0+N−

    1;

    N≧

    2) for one or more selected flights (q) of one or more aircraft;

    (ii) for each continuous-valued parameter p(t;

    k1) of each flight, numbered k1=1, . . . , K1 (K1≧

    0), and for a selected sequence of the times t=tn (n=n0, n0+1, . . . , n=n0+N−

    1, providing a polynomial approximation p(t;

    k1;

    app)=a (tn0;

    k1)+b (tn0;

    k1)·

    (t−

    tn0)+c(tn0;

    k1).(t−

    tn0)2+e(tn0;

    k1), where e(tn0;

    k1) is an error term, whose sum of the squares d(tn0;

    k1)=(N−

    3)

    1


    e(tn;

    k1)2, is minimized by the choice of the terms a(tn0;

    k1), b (tn0;

    k1) and c(tn0;

    k1);

    (iii) forming vectors A={a(tn0;

    k1)}n0, B={b(tn0;

    k1)}n0,C={c(tn0;

    k1)}n0, and D={d(tn0;

    k1)}n0, forming an M1×

    1 vector E1 including a first order statistic m1(v), a second order statistic m2(v), a minimum value min(v) and a maximum value max(v) for each of the vectors v=A, v=B, v=C and v=D;

    (iv) for each discrete-valued parameter, numbered k2=1 . . . , K2 (K2≧

    0) and having L(k2) discrete values, and for the selected sequence of times, forming an L(k2)×

    L(k2) matrix whose entries are the number of transitions between any two of the L(k2) discrete values of this parameter, dividing each of the original diagonal entries by a sum of the original diagonal entries of the L(k2)×

    L(k2) matrix to form a modified L(k2)×

    L(k2) matrix, and forming an L×

    1 vector E2 of entries from the modified L(k2)×

    L(k2) matrices, where L is the sum of the values L(k2)2;

    (v) forming an M×

    1 data vector E with entries including m1(v), m2(v), min(v) and max(v) for each of the vectors v=A, v=B, v=C and v=D, and including the entries of the modified L×

    1 vector, where M=M1+L;

    (vi) computing a covariance matrix F=cov(E);

    (vii) computing eigenvalues, λ



    1, λ

    2, . . . , λ

    M, for an equation F·

    V(λ

    )=λ

    V(λ

    ), where λ

    1≧

    λ

    2≧

    . . . ≧

    λ

    M; and

    (viii) computing a transformed matrix G=DM·

    F, where DM is a selected data matrix.

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