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utf_smooth1

PURPOSE ^

UTF_SMOOTH1 Smoother based on two unscented Kalman filters

SYNOPSIS ^

function [M,P] = utf_smooth1(M,P,Y,ia,Q,aparam,h,R,hparam,alpha,beta,kappa,mat,same_p_a,same_p_h)

DESCRIPTION ^

UTF_SMOOTH1  Smoother based on two unscented Kalman filters

 Syntax:
   [M,P] = UTF_SMOOTH1(M,P,Y,[ia,Q,aparam,h,R,hparam,,alpha,beta,kappa,mat,same_p_a,same_p_h])

 In:
   M - NxK matrix of K mean estimates from Kalman filter
   P - NxNxK matrix of K state covariances from Kalman Filter
   Y - Measurement vector
  ia - Inverse prediction as a matrix IA defining
       linear function ia(xw) = IA*xw, inline function,
       function handle or name of function in
       form ia(xw,param)                         (optional, default eye())
   Q - Process noise of discrete model           (optional, default zero)
   aparam - Parameters of a                      (optional, default empty)
   h  - Measurement model function as a matrix H defining
        linear function h(x) = H*x, inline function,
        function handle or name of function in
        form h(x,param)
   R  - Measurement noise covariance.
   hparam - Parameters of h              (optional, default aparam)
   alpha - Transformation parameter      (optional)
   beta  - Transformation parameter      (optional)
   kappa - Transformation parameter      (optional)
   mat   - If 1 uses matrix form         (optional, default 0)
   same_p_a - If 1 uses the same parameters 
              on every time step for a   (optional, default 1)
   same_p_h - If 1 uses the same parameters 
              on every time step for h   (optional, default 1) 

 Out:
   M - Smoothed state mean sequence
   P - Smoothed state covariance sequence
   
 Description:
   Two filter nonlinear smoother algorithm. Calculate "smoothed"
   sequence from given extended Kalman filter output sequence
   by conditioning all steps to all measurements.

 Example:
   [...]

 See also:
   UKF_PREDICT1, UKF_UPDATE1

CROSS-REFERENCE INFORMATION ^

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