Hi,
I am reading a paper on particle filter on bearing estimation. The system
model is:
x_k = P*x_k-1 + w_k
P is a 4X4 matrix. x_k is the position and speed of a 2-D of the object
position (a, a^, b, b^).
The measurement z_k is:
z_k=tan^-1(b_k/a_k) + v_k
w_k and v_k are Gaussian noise.
On particle filter, it needs likelihood to calculate posterior probability:
p(z_k|x_k)
z_k is a scalar, while x_k is a vector. The pdf of p(z_k|x_k) would be very
complicated analytically. I think I may be in the wrong direction on this
problem. The original problem was described at page 4 of this link:
http://www.ece.iastate.edu/~namrata/EE520/Gordonnovelapproach.pdf
Could you tell me how to deal with likelihood p(z_k|x_k) in particle filter?
Thanks,