2. Suppose we have the following observation model: x = Hs+n, = [X1, X2, ..., XN], H is a known matrix, s is 0-mean where x is the vector of observations, x = vector with covariance matrix Rs, and n is N(0, R₂). a) Derive the MMSE estimate for s. b) Use the orthogonality principle to compute the MMSE (the error) for the estimator from part a).

Linear Algebra: A Modern Introduction
4th Edition
ISBN:9781285463247
Author:David Poole
Publisher:David Poole
Chapter3: Matrices
Section3.7: Applications
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2. Suppose we have the following observation model:
X= Hs +n,
where x is the vector of observations, x [x1, x2, ..., XN], H is a known matrix, s is 0-mean
vector with covariance matrix R., and n is N(0, R,).
a) Derive the MMSE estimate for s.
b) Use the orthogonality principle to compute the MMSE (the error) for the estimator
from part a).
Transcribed Image Text:2. Suppose we have the following observation model: X= Hs +n, where x is the vector of observations, x [x1, x2, ..., XN], H is a known matrix, s is 0-mean vector with covariance matrix R., and n is N(0, R,). a) Derive the MMSE estimate for s. b) Use the orthogonality principle to compute the MMSE (the error) for the estimator from part a).
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