Scilab Function

lqe - linear quadratic estimator (Kalman Filter)

### Calling Sequence

  `[K,X]=lqe(P21)`

### Parameters

- `P21` : `syslin` list
- `K, X` : real matrices

### Description

`lqe` returns the Kalman gain for the filtering problem in continuous or discrete time.

`P21` is a `syslin` list representing the system `P21=[A,B1,C2,D21] P21=syslin('c',A,B1,C2,D21) or P21=syslin('d',A,B1,C2,D21)`

The input to `P21` is a white noise with variance:

         [B1 ]               [Q  S]
    BigV=[   ] [ B1' D21'] = [    ]
         [D21]               [S' R]

`X` is the solution of the stabilizing Riccati equation and `A+K*C2` is stable.

In continuous time:

    (A-S*inv(R)*C2)*X+X*(A-S*inv(R)*C2)'-X*C2'*inv(R)*C2*X+Q-S*inv(R)*S'=0

    K=-(X*C2'+S)*inv(R)

In discrete time:

    X=A*X*A'-(A*X*C2'+B1*D21')*pinv(C2*X*C2'+D21*D21')*(C2*X*A'+D21*B1')+B1*B1'

`K=-(A*X*C2'+B1*D21')*pinv(C2*X*C2'+D21*D21')`

`xhat(t+1)= E(x(t+1)| y(0),...,y(t))` (one-step predicted `x` ) satisfies the recursion:

    xhat(t+1)=(A+K*C2)*xhat(t) - K*y(t).

### Examples

    //Assume the equations
    //.
    //x = Ax + Ge
    //y = Cx + v
    //with
    //E ee' = Q_e,    Evv' = R,    Eev' = N
    //
    //This is equivalent to
    //.
    //x = Ax  + B1 w
    //y = C2x + D21 w
    //with E { [Ge ]  [Ge v]' } = E { [B1w ] [B1w D21w]' } = bigR =
    //         [ v ]                  [D21w]
    //
    //[B1*B1'  B1*D21';
    // D21*B1'  D21*D21']
    //=
    //[G*Q_e*G' G*N;
    // N*G' R]

    //To find (B1,D21) given (G,Q_e,R,N) form bigR =[G*Q_e*G' G*N;N'*G' R].
    //Then [W,Wt]=fullrf(bigR);  B1=W(1:size(G,1),:);
    //D21=W(($+1-size(C2,1)):$,:)
    //
    //P21=syslin('c',A,B1,C2,D21);
    //[K,X]=lqe(P21);

    //Example:
    nx=5;ne=2;ny=3;
    A=-diag(1:nx);G=ones(nx,ne);
    C=ones(ny,nx); Q_e(ne,ne)=1; R=diag(1:ny); N=zeros(ne,ny);
    bigR =[G*Q_e*G' G*N;N'*G' R];
    [W,Wt]=fullrf(bigR);B1=W(1:size(G,1),:);
    D21=W(($+1-size(C,1)):$,:);
    C2=C;
    P21=syslin('c',A,B1,C2,D21);
    [K,X]=lqe(P21);
    //Riccati check:
    S=G*N;Q=B1*B1';
    (A-S*inv(R)*C2)*X+X*(A-S*inv(R)*C2)'-X*C2'*inv(R)*C2*X+Q-S*inv(R)*S'

    //Stability check:
    spec(A+K*C)

### See Also

`lqr` , `observer` ,

### Author

F. D.