How Do You Model a Discrete Stochastic Signal Using State-Space Representation?

AI Thread Summary
The discussion focuses on modeling a discrete stochastic signal using state-space representation, specifically addressing the equation s(k) = w(k-1) + aw(k-2) with white Gaussian noise. The user initially struggles with the state-space formulation due to the absence of an autoregressive (AR) component, questioning the role of the A-matrix and the specified state variables. Clarifications are sought on how to properly incorporate the given states into the model. Ultimately, the user reports a resolution to their confusion, indicating they have successfully solved the problem. This highlights the importance of understanding state-space representation in stochastic signal processing.
mr.t
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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, \sigma_v^2=1. v(n) and w(n) are independant.

Problem: Find the space-state model:
x(k+1) = Ax(k) + Bw(k)<br /> y(k) = Cx(k) + v(k)

By using the state:
x(k) = \bmatrix s(k) \\ w(k-1) \endbmatrix

Homework Equations


(given above)

The Attempt at a Solution


I have only solved these problems when there is a AR-part. As this is an ARMA(0,2) I have no clue and need help. If its just an MA-part, then the whole A-matrix is zero? And how should I use the fact that I am suppose to use the specified states? How does that affect the state-space model?

Im confused, please help me!
Thanks!
 
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Just want to let you guys know that I've solved it. (pretty sure at least :-p)
 
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