EKF_MCDA_UPDATE EKF Monte Carlo Data Association Update
Syntax:
[S,C] = EKF_MCDA_UPDATE(S,Y,H,R,IM,V,TP,CP,CD,param)
In:
S - 1xN cell array containing particle structures
Y - Measurement as Dx1 matrix
H - Derivative of h() with respect to state as matrix,
inline function or name of function in
form H(x [,P1,P2,...])
R - Measurement noise covariance.
IM - Mean prediction (innovation) as vector,
inline function or name of function in
form h(x [,P1,P2,...]). (optional, for default see EKF_UPDATE)
V - Derivative of h() with respect to noise as matrix,
inline function or name of function in
form V(x [,P1,P2,...]). (optional, for default see EKF_UPDATE)
TP - Tx1 vector of prior probabilities for measurements
hitting each of the targets. (optional, default uniform)
CP - Prior probability of a measurement being due
to clutter. (optional, default zero)
CD - Probability density of clutter measurements,
which could be for example 1/V, where V is
the volume of clutter measurement space. (optional, default 0.01)
param - Parameters of H,h, and V.
Out:
S - 1xN cell array containing struct arrays of updated particles
C - 1xN vector of contact indices 0...T, where 0 means clutter
Description:
Perform update step for a Monte Carlo Data Association
filter which uses Extended Kalman Filter as estimator
for the subproblem, where associations are known. Filter
can be used for tracking multiple objects and modeling
of clutter measurements.
The model is
{ c_d, if clutter
y ~ { N(y | h(x{i}), R) , if measurement from target i
Resampling is NOT performed.
See also:
EFF_WEIGHTS, RESAMPLE, EKF_UPDATE, KF_UPDATE