Recent content by mr.t

Just want to let you guys know that I've solved it. (pretty sure at least :-p)

Homework Statement A time discrete stocastic signal is described by s(k) = w(k-1) + aw(k-2), |a|<1 and w(n) is white gaussian noise with m_w = 0, \sigma_w^2 = 1. It is observed under influence of white noise: y(k) = s(k) + v(k) where v(n) is white gaussian noise with m_v = 0...

It could be an error in the solution then no fun try to learn stuff when you end up spending your time figuring out the imposible... thanks for your time anyway!

Hello Tiny-tim! Putting the a^{2}P to the left and multiply both sides with (P+1) gives: (P+1)(P-a^{2}P) = b^{2}P^{2} \Rightarrow \Rightarrow P^{2}-a^{2}P^{2}+P-a^{2}P-b^{2}P^{2} = 0 \Rightarrow \Rightarrow P^{2}(1-a^{2}-b^{2})+P(1-a^{2}) = 0 But I still can't come to the final solution? :S

Homework Statement What is the steps between the two equations? Homework Equations P = a^{2}P+b^{2}P^{2}/(P+1) \Rightarrow P^{2}-(a^{2}+b^{2}-1)P-b^{2} = 0 Thanks!

Ok my bad. Seems like you don't even calculate x(n|n|) in the recursion, you just calculate P(n|n-1), K(n) and P(n|n) for each step (!). But this leads to another question. When you have done your iterations and found that your P:s and K:s are getting steady, they you have your Kalman gain as...

As this is concerning signal processing i guess this is the right place to post the question. I am Trying to learn how to use kalman filters. I've reached some form of verry basic understanding of the state-space model but I am still kindof confused. What I am trying to do now is to understand...