KF_PREDICT Perform Kalman Filter prediction step Syntax: [X,P] = KF_PREDICT(X,P,A,Q,B,U) In: X - Nx1 mean state estimate of previous step P - NxN state covariance of previous step A - Transition matrix of discrete model (optional, default identity) Q - Process noise of discrete model (optional, default zero) B - Input effect matrix (optional, default identity) U - Constant input (optional, default empty) Out: X - Predicted state mean P - Predicted state covariance Description: Perform Kalman Filter prediction step. The model is x[k] = A*x[k-1] + B*u[k-1] + q, q ~ N(0,Q). The predicted state is distributed as follows: p(x[k] | x[k-1]) = N(x[k] | A*x[k-1] + B*u[k-1], Q[k-1]) The predicted mean x-[k] and covariance P-[k] are calculated with the following equations: m-[k] = A*x[k-1] + B*u[k-1] P-[k] = A*P[k-1]*A' + Q. If there is no input u present then the first equation reduces to m-[k] = A*x[k-1] History: 26.2.2007 JH Added the distribution model for the predicted state and equations for calculating the predicted state mean and covariance to the description section. See also: KF_UPDATE, LTI_DISC, EKF_PREDICT, EKF_UPDATE

- imm_filter IMM_FILTER Interacting Multiple Model (IMM) Filter prediction and update steps
- imm_predict IMM_PREDICT Interacting Multiple Model (IMM) Filter prediction step
- imm_smooth IMM_SMOOTH Fixed-interval IMM smoother using two IMM-filters.
- kf_loop KF_LOOP Performs the prediction and update steps of the Kalman filter
- tf_smooth TF_SMOOTH Two filter based Smoother
- uimm_predict IMM_PREDICT UKF based Interacting Multiple Model (IMM) Filter prediction step
- uimm_smooth UIMM_SMOOTH UKF based Fixed-interval IMM smoother using two IMM-UKF filters.

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