Scilab Function

lqe - linear quadratic estimator (Kalman Filter)

### Sequence d'appel

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

### Parametres

- `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]`

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).

### Voir aussi

`lqr`

### Auteur

F. D.