iekf

iekf(y, t, is_artifact, m, num_iters, num_particles, verbose)

IEKF estimates a multi-peak, state-space model for a spectrogram using

the iterated extended Kalman filter augmented to estimate the discrete transition of the On/Off-peak combination and to draw an improved initial reference trajectory. It selects the predictive draw of maximum likelihood as the intial reference trajectory.

NOTE: This function explicitly overwrites the state transition by

incorporating the 0.9 factor into Phi and using this Phi for both the state and state-error covariance updates.

INPUTS:

y – observed spectrogram (dim_y x T) t – time points of spectrogram (1 x T) is_artifact – indicator vector of time points of artifacts (1 x T) m – StateSpaceMultiPeak object containing the model num_iters – positive integer number of iterations.

EKF version given by 1. Default 20.

num_particles – positive integer number of draws.

Version without draws given by 1. Default 500.

verbose – flag indicating level of verbosity. Default 2.

0 - no display of progress. 1 - text display of progress 2 - graphical display of fits as it progresses.

OUTPUTS:

ss_etim – estimate structure for StateSpaceMultiPeak.

Contains:

num_particles num_iters xf_hat - filter state estimates (dim_x x T+1) Pf - filter state error covariances (dim_x x dim_x x T+1) yf_hat - filter observation estimates (dim_y x T+1) comps_hat_f - filter component peak estimates (dim_y x T+1) alpha - filter estimate of On/Off-peak combination (num_combos x T+1) xp_hat - prediction state estimates (dim_x x T+1) Pp - prediction state error covariances (dim_x x dim_x x T+1) yp_hat - prediction observation estimates (dim_y x T+1) comps_hat_p - prediction component peak estimates (dim_y x T+1)

implements iterated extended Kalman filter as per Jazwinski

Theorems 8.1 and 8.2 but augmented to

Created by Patrick Stokes and Michael Prerau Created on 2017-04-20 Modified on 2017-04-24