EKF_MCDA_PREDICT EKF Monte Carlo Data Association Prediction
Syntax:
S = EKF_MCDA_PREDICT(S,A,Q,AM,AW,param)
In:
S - 1xN cell array containing particle structures.
A - Derivative of a() w.r.t. state
as matrix if common for all targets or
as cell array of size TxN, for each target in
each particle. Can also be a inline function or name of
function in form A(x,i,param), where i
is the index of the target x. (optional, default eye())
Q - Process noise of discrete model as matrix
if common for all targets, or as cell array
of size Tx1 for all targets separately. (optional, default zero)
a - Dynamical model as cell array
of size TxN, for each target in each particle,
or inline function or name of function in
form a(x,i,param), where i is the
index of the target x. (optional, default A(x,i)*X)
AW - Derivative of a w.r.t. q
as matrix if common for all targets or
as cell array of size TxN, for each target
in each particle. Can also be a inline function or name
of function in form AW(x,i,param), where
i is the index of the target x. (optional, default identity)
param - Parameters of A and a.
Out:
S - 1xN cell array containing the struct arrays of predicted particles.
Description:
Perform Extended Kalman Filter prediction step for each target
and each association hypothesis particle. The model is
x_i[k] = a_i(x_i[k-1], q , param), q ~ N(0,Q_i)
for each target i. Dynamics a_i() for each target
are assumed to have known statistics.
See also:
EKF_MCDA_UPDATE, EKF_PREDICT, LTI_DISC, KF_PREDICT