Dear All,
I had a query regarding using Bayes++ Bayesian Filter Classes. I am trying
to use Bayes++ classes to implement a Nearly Coordinated Turn (NCT) Model
Kalman filter. As this dynamics are non-linear in nature, I need to use the
Extended Kalman Filter (EKF). I am using, Covariance_scheme from the Bayes++
classes.
In this for the predict the code is as follows:
Bayes_base::Float Covariance_scheme::predict (Linrz_predict_model& f)
/* Standard Linrz prediction */
{
// Extended Kalman state predict is f(x) directly
x = f.f(x);
// Predict state covariance
noalias(X) = prod_SPD(f.Fx,X, tempX);
noalias(X) += prod_SPD(f.G, f.q, tempX);
return 1;
}
In this I can implement f(x) in my predict class, so as to give the
linearized update to state. However, the state matrix Fx also requires
re-computation as the jacobian of f(x) at x(k|k), and this needs to be
updated too before call to predict. However, I am unable to figure how this
is catered for in Bayes++, because f(x) is a const function, I cannot update
Fx in f(x).
Could you kindly clarify the same?
Thank you for your time.
Regards,
Sunil Chomal
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